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Allyson Klein and Jeniece Wnorowski chat with Kelley Osburn of Graid about SupremeRAID™ and its role in tackling high-performance storage challenges in data-driven environments.

There can be an attitude that people who are partner managers are not as sensitized to achieving revenue, developing products and services or the challenges of taking them to market. Don’t ever believe that industry partnerships have little real effect on company revenues or market successes. The people in those roles must know their own corporate business and their partners' business drivers for anything of consequence to be accomplished. Let me walk you through the “why” behind that claim.

Everyone has a boss with authority to “fire” them.

I will start with the argument I’ve heard many times: “I can’t wait until I save up enough money so I can run my own company, no one can fire me!” Unless you are wealthy enough to pour money into a company that can’t cover its operating costs, your customers are your boss. They get to vote with their wallets and “fire” you by not buying from you at all.

A Board of Directors has “bosses” – shareholders, stakeholders and regulators. Shareholders can “fire” directors in a few ways – hostile takeovers, purchasing enough stock to have a voting seat on the board then voting everyone else off the board, or dramatically selling off the company’s stock from their investment portfolios. There are three primary duties for BOD members: Duty of care (diligence in being informed and involved), Duty of Loyalty (prioritize the corporations’ best interests along with its shareholders) and Duty of Obedience (personal and corporate actions comply with local, state and federal regulations).

CEOs have a boss – The Board of Directors. Two top failures by a director in exercising their fiduciary responsibilities are failing to oversee management and making decisions without adequate information. The implication is that a good director prioritizes preventative involvement with a CEO.

It is not “just business.”

Humans are social/relational – even engineers! Regardless of the title or level, humans all have similar fears, hopes and ambitions. In many cases, the fear of failure or being exposed as an imposter is highest in the C-suite compared to the working level.

Everyone has goals set by their “bosses” that advance personal success within the company or cause personal loss of capital with their management chain. Are you approaching a given partnership as if more than your goals matter?

Ask yourself if there is a way to help increase your counterparts’ chances of success while executing the work you want to accomplish with them. Understand the business they are in, how they are working to improve their profitability, and their brand value to their customers. Know whether your success results in their success – at both an individual level and a corporate level.

Do the math. Literally.

Over a decade ago I was responsible for executing a previously negotiated agreement between my company execs and a critical partner’s executives. My counterpart and I started hashing out how to adhere to the agreement while advancing the goals each company had for the partnership.

Just one tiny innocuous comment from my GM caused me to dig a little deeper after some puzzling behavior from my counterpart at our partner: “Don’t worry – they make so much more money with us; they have no interest in stalling our partnership in favor of other actions.”

I did the math. I truthfully hoped to calculate how much more money they would make from our partnership to present that in ongoing negotiations when I instead confirmed they did NOT make more money in our partnership as written. This explained why my counterpart, who like me was part of the pre-close negotiations, was so uncomfortable with the suggestions I made. Inadvertently, I was asking that individual to expend significant capital with their management chain, who were accountable to the CEO, who was accountable to their BOD, to shareholders, on down the line.

As much as my counterpart appreciated me as a person and valued the way I collaborated, they were unable to execute against many of the items my company hoped for. An action from my counterpart that either hurt or simply failed to advance their company’s overall business interests would ultimately be unwound.

Concluding thoughts

Industry partnerships are some of the most rewarding experiences I have had, because the sense of joint accomplishment in executing something that moved everyone’s business forward has very few equals. To take it to the next level, be a student of your own company’s business as well as that of your partner’s. You would be truly astounded at how often these simple concepts are overlooked, and surprised by what you discover when applying them to your own situation.

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Arun Nandi of Unilever joins host Allyson Klein to discuss AI's role in modern data analytics, the importance of sustainable innovation, and the future of enterprise data architecture.

Join Allyson Klein as she welcomes former colleague/ industry innovator Jen Huffstetler. Jen shares her extensive experience driving advancements from client devices to the data center, including groundbreaking technologies like Centrino and 3D packaging.

With GPU-driven AI training ruling the moment, we have finally come to the asymptotic moment for liquid cooling to overtake air cooled data center infrastructure for many environments. Consider, for a moment, that NVIDIA Blackwell-based racks are drawing from 60kW to 120kW per rack, a dramatic shift from the historic 5-10kW per rack delivered to fuel general purpose applications. When you extrapolate that power across football fields of racks for a hyperscale training cluster, you realize that there’s a LOT of heat to extract. The debate has quickly shifted from air vs liquid to what type of liquid to utilize, opening the door for market disruption and new player entry.

This is why I was so excited to talk to Dr. Kelley Mullick, vice president of technology advancement at Iceotope. Kelley joined Iceotope, a Sheffield, England-based immersion cooling startup, last year, bringing with her a technology leadership pedigree and the notable achievement of having delivered the first industry liquid cooling warranty while at Intel in 2022. Her PhD in chemical engineering and lengthy engagement in industry standards work places her squarely in the middle of liquid cooling advancement.

So why liquid cooling? Kelley confirmed that AI is the primary driver for urgency in transition to liquid cooling due to its serial computing nature, but also stated that broader commitments to sustainability have driven hyperscalers to consider liquid alternatives. She outlined the three alternatives in play in the liquid market: cold plate, tank immersion and precision liquid cooling. While all are more effective and efficient than air, each of the alternatives offer different advantages for consideration. Cold plate has the advantage that it has been widely deployed in HPC environments and utilizes air to cool parts of the chassis where liquid plates are not uniquely targeted, supporting retrofit opportunities for existing infrastructure. Tank immersion delivers a solution where heat can be captured for secondary usage but is also delivered at a weight that requires reinforcement of flooring in existing data center tile flooring, likely limiting to greenfield buildouts. Finally, precision liquid is somewhat of a hybrid, offering advantages of immersion cooling with alternative chemistries to water and similarities to cold plate, offering deployment in existing vertical racks.

If this complexity wasn’t enough, there’s also the topic of chemistry, and it’s here that Kelley really lit up. To start, the options for liquid cooling are water (used in cold plate designs) and dielectric fluid (used in cold plate, immersion, and precision designs). Dielectric fluid is composed of hydrocarbon or fluoridated hydrocarbon fluid with most vendors targeting hydrocarbon options because of its non-toxic composition and ability to be recycled. For two phase cooling solutions, however, only fluoridated hydrocarbon solutions can be used, introducing toxic chemicals into the data center and representing increased challenges from a circularity perspective.

Iceotope is delivering a pretty special chemistry within this landscape. Kelley explained that solutions are delivering precision cooling at up to 1500 watts with thermal resistance 0.037 Kelvin/watt, at par with fluoridated solutions with a sustainable and environmentally friendly chemistry. This technology is delivered in adaptable form factors including racks, power shelves and more, enabling customers to deploy across data center and edge environments. Kelley also noted that different types of infrastructure from GPUs and CPUs to storage JBODs can be submerged in dielectric fluid. Iceotope has done extensive testing of material compatibility to ensure customer deployments will keep cool without reliability erosion.

What’s the TechArena take? We were delighted that we were able to feature this story on our Data Insights series sponsored by Solidigm as cooling is critical to delivery of the data pipeline. Iceotope is delivering disruptive technology in this space, and I expect to hear much more about their solutions as we head into the OCP Summit this fall. If liquid cooling is not on your radar today…put it on your radar. With hyperscalers moving rapidly to liquid alternatives, we expect solutions to scale to meet edge requirements and broader scale AI configurations in data centers. To learn more, check out the interview and visit Iceotope’s site.

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Why Wait? Sometimes That’s the Only Choice

“All good things arrive unto them that wait - and don't die in the meantime.”

-   Mark Twain

Memory is always a tricky thing. And we’re not just talking about trying to find the TV remote for five minutes only to finally discover that it’s in the fridge next to the five-pack. Don’t judge until you’ve done your 1.6 km in a certain pair of size 14’s, thank you. Anyway, Your Humble Author (YHA) remembers many a discussion on system memory, cache topologies, drive characteristics, and all those other fun things that are associated with an engineer’s most-dreaded, four-letter “L” word: latency. During one particular discussion with a senior software VP at a large OS company, he opined, “All processor architectures wait equally fast.” So, so true.

Pretty much every one of those computer architectures arranges the logic units to operate on a series of data registers. We promise this is going somewhere… and we’re also simplifying a lot so everyone can grab a bone and pick. Data registers load and store from memory. Some architectures will also allow for direct memory operations. But which memory? This is where waiting comes to play.

The following analogy has been changed to protect the innocent, but YHA thanks the smart people for the concept. Let’s say the desired memory is stored in the nearest cache (with typical latencies in the one nanosecond range these days). Let’s also say that acquisition is the equivalent of finding the TV remote again next to the three-pack. Easy. Processor architectures typically have a series of larger caches or on-chip memories these days, followed by DRAM on the motherboard. It could take a walk to the neighbor’s house for a quick wave while opening the fridge in the garage to accomplish that particular DRAM “fetch.”

If that particular data happens to be stored on some of the latest and best solid-state memory on a coherent bus, the equivalent would be a short drive to the local convenience store. Leave the remote at home, please. A slightly older SSD configuration – or a poor driver routine (processor people always blame the software) – could mean a slightly longer trip to the grocery store in town. The average AI cluster data transfer across the mesh would involve stopping for a sit-down dinner before heading home. That’s a lot of work for one individual piece of data, all while the particular system thread is waiting.

One more. The best network topologies in the datacenter these days usually guarantee a maximum of 5 milliseconds latency from any server to your register. THE COMPANY where YHA used to work offered the promise of an 8-week sabbatical. Were that entire sabbatical devoted to the acquisition of one beverage unit, we’d be in the ballpark of the latency we’re discussing. That sucker better not be an over-hopped IPA. And what’s the system thread doing that whole time? To use the Yiddish: bupkis.

Clearly, those decades of work between hardware and software geeks on multi-threading, data-ordering, data promotion, appropriate memory and SSD sizing, (keep going, it was decades) were all pretty vital. The modern datacenter rack topology is now largely being driven by collaborative, hive-mind organizations like the Open Compute Project (OCP). And even with all that effort, many of those cores are still essentially thirsting for that next drink of data. There’s a very cogent person a few TechArena articles away from this one that noted it can be very disappointing to pay $1B for some AI hardware only to see $500M of work happen. These are not inconsequential decisions.

So what to do? First, don’t panic (towel and exploding planet optional). There are plenty of resources out there that can help optimize whatever you’re building. Look for similar organizations in your region or technology circle and ask questions, many will be very open if they’re not competing with you, some even if they are. Look to organizations like OCP as a guide for setting a proper configuration. Also, your software providers likely have a much better view of configurations that best run their work, mostly because of those decades of work. Finally, look to cloud options, since your provider will then be taking the risks.

And keep the analogy going. Maybe in the flat world of data analytics we’ll collectively find out what data fetch has us all going to the moon and back. By the way, is anyone else thirsty? And where the heck is the remote?

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“If a machine is expected to be infallible, it cannot also be intelligent.”

-   Alan Turing

“Shall we play a game?”

-   Joshua, War Games

Total aside that will become common in this space. We’ve been out camping to celebrate our 28th anniversary. How the heck she’s tolerated me that long is a story in itself, THE COMPANY only stood me for 26.5!

We were short on a bottle for the pre-dinner drink. Thirty minutes to the closest liquor store (relax, we needed groceries, too), and we found a Rebel 100 (Lux Row Distillers, Bardstown, KY). It’s made using a traditional wheated recipe. The correspondent and the lovely bride both have a fondness for the grassiness wheat adds, and there’s a little beyond-bourbon sweetness that you normally don’t get in a 100 proof taste. The smokiness might come from the barrel, or it might be that the AQI is well into the 200 range because half of Oregon is on fire. Anyway, cheers.

Your Humble Author (YHA) has a fascination with the stock market, mostly because compound interest provides to us, the willfully underemployed. One particular amusement these days is watching all the little Whos in Whoville screaming, “But we have AI!” when being dragged to the dust speck boiler of earnings results. First off, a quarter of the time it’s not AI, stop lying to your marketing department. We get that Machine Learning is esoteric and hard to explain, but it also generates results you can audit. Another quarter involves using someone else’s chatbot to produce not very meaningful results. That remaining half? The development department was looking for some “me time” and figured that a 60% accuracy rate was better than the dartboard they usually get from upper management, so they did an implementation hoping to get Friday afternoons off. There’s some good, but there’s a lot of not good.

AI has been around for a long time, the Turing Test is named after a really smart guy who passed in the 1950’s, after all. Some of us would probably say that the current rush on AI is due to the accumulation of data, but that’s a little misleading. There’s always been data sloshing around the world. It’s just that very little of it was in the right place, and it certainly was not in the right structure to be scrutinized. If some government-funded project wanted to study global temperatures, it had to do the work of getting the data from every ledger to one computer storage spot, in one format, with some serious programmers to write serious code to produce meaningful results.

The PC/Internet revolution of the 90’s started the ball rolling on creating a “place” for everything. After a couple decades of upper management demanding that everything go up in the cloud, we finally solved the access problem, but not the structure one. If that now bigger government-funded project wanted to take a look now, it’s not only got the old temperature data, but access to a billion backyard weather stations, all the articles about the prior art, and a serious amount of social media memes vamping on a repetitive Nelly tune. So now it’s down to figuring out how to access all the slosh in that giant container.

A more relevant aside than usual: YHA remembers being at an IBM Conference a few years back where a Watson executive told the audience that they were tracking potential flu epidemic outbreaks in part by monitoring Twitter for people posting the equivalent of, “I feel like crud today.” They were finding outbreaks about ten days faster than the CDC who was tracking their standard measures like hospital data and pharmacy orders. So this AI stuff CAN work if you know what you’re doing.

Anyway, back when we were all calling it Big Data ten years ago, the structure problem was still a limiting factor in the analytics discussion. We were all about the V’s: Volume, Variety, Velocity. The real winning formula in analytics at that point was addressing the Variety by referencing more and more data types in their natural form, which eliminated the need for costly (money and time) Extract/Transform/Load (ETL) routines. We were right back to those serious programmers, serious code, meaningful if directional results.

Artificial Intelligence is a fundamentally lazy approach to the data problem. Lest you think that’s a condemnation, any good strategist will tell you that the best strategies are ones that are lazy, the hard work is in the tactics. The basic approach of AI is to throw it at all the data and see what patterns it finds. Structure is less of an issue when your algos are seeking repetition independent of prior relevance. So all those companies claiming that AI will solve your problems are likely correct in the long term. Except…

All you data nerds have been thinking: “Dude, you forgot a V a couple paragraphs ago.” You’d be correct. As the data in the cloud continued to grow, we added Veracity to the equation. The sober geeks out there are still sifting the data before they attack it with AI, knowing that garbage in still produces garbage out. Those less experienced, or less aware that tactics are where the hard work hits, are likely to slow their actual decision processes by getting inconsistent results. If you’d like a practical example, hit up any chatbot or AI search engine with a conspiracy theory that goes against your grain, and discover the chaos it creates.

So financial results won’t actually just come at the behest of, “We have AI!” They’ll come when companies build in the tools and training to sort the data and make the sober decisions. We’ll all be able to see this shake out in front of us, even as the new types of data hitting the cloud create a need to do it all over again in a more complex way. As always when dealing with your own personal compounding, caveat emptor, and happy hunting.

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In a recent episode of the TechArena Data Insights series, my co-host Jeniece Wnorowski and I had an insightful conversation with Ariel Pisetzky, the Vice President of Information Technology and Cyber at Taboola, about the transformative impact of data and AI on ad placement. Our discussion revealed how these advanced technologies are redefining the advertising landscape, making ad placements more efficient and targeted to business objectives.

The Shift to AI-Driven Advertising

Ariel emphasized that at Taboola, the mission is to "connect people with content they may like but never knew existed." This mission is powered by sophisticated algorithms that analyze user behavior, preferences, and context to deliver highly personalized content. Ariel noted, "Our systems are designed to process enormous amounts of data in real-time to understand user intent and deliver the most relevant ads." As someone whose business in part is driving micro-targeted paid media, I was delighted to learn from Ariel about what he and the team at Taboola are delivering.

I was also delighted to hear that AI is at the core of Taboola's strategy for ad placement. By utilizing machine learning models, Taboola can predict which ads are most likely to engage individual users. Ariel explained, "Our AI systems analyze vast amounts of data in real-time to understand user intent and preferences. This allows us to serve ads that are not only relevant but also engaging."

These AI algorithms take into account various factors, including browsing history, time of day, and even the type of device being used, ensuring that ads are placed in the optimal context, thus maximizing user interaction.

Dynamic and Contextual Ad Placements

One of the key innovations we discussed with Ariel is Taboola's approach to dynamic and contextual ad placements. Traditional ad placement strategies often rely on static parameters, but Taboola's AI-driven platform can adapt in real-time. For instance, if a user frequently reads tech blogs in the evening, Taboola's system might prioritize tech-related ads during that time frame.

Ariel highlighted this capability, stating, "Dynamic ad placements allow us to adjust the content based on immediate user context. This not only improves the user experience but also enhances ad performance for our clients."

Predictive analytics is another area where Taboola excels. By analyzing historical data and user behavior patterns, the platform can forecast future actions and preferences. This predictive power enables advertisers to stay ahead of trends and tailor their campaigns accordingly.

As Ariel mentioned, "Predictive analytics gives us a glimpse into what users might be interested in next. This foresight is invaluable for creating timely and relevant ad campaigns that capture user interest before it peaks."

Optimized Platforms Deliver to Taboola’s Vision

In our discussion. Ariel highlighted the collaboration between Taboola and Solidigm, focusing on how their combined efforts are enhancing data management and AI capabilities. Ariel mentioned that Solidigm’s advanced data storage solutions play a crucial role in supporting Taboola's AI infrastructure. He noted, "Solidigm's innovations in data storage technology have allowed us to manage and process vast amounts of data more efficiently, which is essential for our AI-driven ad placement systems."

Ariel further explained that the high-performance and reliability of Solidigm's storage solutions ensure that Taboola's AI models can access and analyze data in real-time, leading to more accurate and timely ad placements. "With Solidigm, we're able to scale our operations and maintain high performance even as our data needs grow," he added. This partnership exemplifies how cutting-edge storage technology can support the demanding requirements of modern AI applications, enabling more effective and personalized advertising strategies.

Challenges and Ethical Considerations

Despite the collective advancements Taboola has made to its platform, Ariel acknowledged the challenges and ethical considerations involved in using AI and data for ad placement. Privacy concerns and data security are paramount, and Taboola is committed to maintaining high standards in these areas. "We are constantly evolving our practices to ensure user data is handled with the utmost care and transparency," he emphasized.

Looking ahead, Taboola plans to further integrate AI capabilities to refine ad placements. The company is exploring the use of deep learning models to enhance content recommendations and improve ad targeting accuracy. Ariel shared, "Our goal is to push the boundaries of what's possible with AI, making our ad placements smarter and more intuitive." I, for one, cannot wait to see what is on the horizon as Taboola continues to spearhead innovation in this important arena for marketers.

For more insights from this episode, you can listen to the full conversation on TechArena's podcast.

About Solidigm:

Data storage requirements are evolving rapidly with the explosion of the AI era, and it's important to find the right partner that can provide the flexibility and breadth for each specific AI application. Solidigm is a global leader in innovative NAND flash storage solutions with a comprehensive portfolio of SSD products based on SLC, TLC, and QLC technologies. Headquartered in Rancho Cordova, California, Solidigm operates as a standalone U.S. subsidiary of SK hynix with offices across 13 locations worldwide.

In this Tech Arena episode, Allyson Klein interviews Mehdi Daoudi, CEO of Catchpoint, on internet monitoring, observability innovations, and AI's impact on automation. Discover their latest tools enhancing performance.

For those who listen to the TechArena podcast, you may recall an episode featuring Urban Machine, a leading startup in the construction reclamation space whose solution creatively combines advanced robotics and AI to “clean” used lumber salvage and ready it for a second use in new building construction. When you consider that up to 25% of landfill is derived from construction materials, it is apparent that solutions that embrace circularity are rapidly adopted. Urban Machine has garnered a lion’s share of attention in this arena given accolades at SXSW and more recently named as a top 10 startup to know by Climate Insider. Urban Machine also collected recognition from the US Green Building Council California with a 2024 Mighty Materials Award underscoring the power of reclaimed materials to green construction practices.

We were curious to check in with Urban Machine on the progress it has made in delivering this breakthrough tech to construction sites. The eager engineers at the company report that they are hard at work readying to deliver their first production machine onsite to customers. Check out this video to check out the latest in deployment advancement. I wouldn’t be surprised at all to see this solution garner more than its unfair share of the >$20 million in US EPA funding for clean manufacturing materials.

Want to learn more about Urban Machine? Check out my podcast with Co-Founder and CTO Andrew Gillies as he describes how the machine has come into being, the technologies behind being able to pinpoint fastener locations in wood, pry fasteners out with minimal wood damage, and deliver a safe product back into construction sites, and how developers can engage with the team to get multiple generations out of lumber.

In this podcast, learn about the challenges of silicon advancement, the importance of advanced packaging, and how Lam is driving breakthroughs to support the future of AI and chiplet ecosystems.

The TechArena are fans of what WEKA is delivering in the market, and we’ve covered their data platform since last year as the company has unveiled innovations to help speed enterprise adoption of AI in market. It was, therefore, no surprise to us to see Contextual AI select WEKA as a strategic partner for delivery of enterprise AI services on Google Cloud this week. Contextual AI has made a name for itself with RAG 2.0, delivering enterprises fine-tuned models that provide foundational tools for enterprises wanting to build and customize specialized AI applications.

When you consider the application of contextual language models, a step beyond traditional RAG with integration across pre-training, fine tuning, and alignment with human feedback. Traditional RAG models uses an off-the-shelf model embedding, vector database for retrieval, and a distinct language model for generation, stitched together through an orchestration framework and therefore limiting both the final value of the application and difficulty and efficiency in delivery of the model.

RAG 2.0 was delivered, you guessed it, by the same leadership team that first delivered RAG at Facebook AI Research, so they know a bit about the RAG approach and how to make it better. RAG 2.0 has delivered proof of benefit across various industry benchmarks showcasing improved accuracy vs. RAG and Vanilla RAG models based on GPT-4 and Mixtral). That’s cool stuff. 

When you consider how much enterprises are focusing on RAG implementations to bring AI to the mainstream, you can understand why Contextual AI is garnering significant attention for delivery of its solution to customers. And when you consider the scale of data required for RAG 2.0, WEKA emerges as a perfect partner to support data pipeline requirements.

WEKA has delivered a fantastic data management platform to optimize GPU utilization delivering peak efficiency in model performance as well as value to the customer funding the Google Cloud instance. Contextual AI has deployed a total of 100TB thus far of WEKA data platform capacity to fuel data requirements and has seen increased developer productivity and faster model training times with the new solution. The delta in performance is eye-opening with a stated 3X performance gain across key AI use cases and 4X faster AI model checkpointing. They’ve done all this while reducing cost 38% per terabyte.

What’s the TechArena take? We love stories about taking technology innovation and actually implementing to customer value. What Contextual AI and WEKA are delivering here are useful tools that enterprises can tap today for real adoption of AI within their environments. We can’t wait to see more advancement on the contextual language models as the industry at large continues its bandwagon of support of RAG type frameworks for AI fine tuning and deployment. And we’re delighted to see WEKA’s data management solutions continue to garner momentum in market for this once-in-a-lifetime moment of AI-era computing advancement.

While Level 3 ADAS (conditional automation) requires the driver to be present and engaged, the resultant heterogeneous workload, a mix of compute and AI processing, is driving the need for new system-level architectures and state-of-the-art system-on-chips (SoCs) based upon leading-edge semiconductor processes and packaging technologies.  More specifically, solutions for Level 3 ADAS and above are driving the automotive industry to embrace chiplets to most efficiently and effectively address the demanding workloads by allocating the right task to the right compute engine optimized for a given task in a footprint that is most efficient from both a power and area perspective.  It is also the case that chiplet-based solutions can be more cost-effective when compared to an equivalent solution based on monolithic technologies.

Chiplet technology, still in its relative infancy, enables disparate technologies and semiconductor dice to be combined in a single package through the use of die-to-die interconnect which is physically connected via a package substrate while enabling performance that is equivalent to that of a monolithic device.     

In March 2022, a universal interface for the interconnection of chiplets was released. Dubbed UCIe 1.0 (Universal Chiplet Interconnect Express), the introduction of this standard, in part, reflects the broader industry awareness that the traditional benefits of scaling through process technology are being challenged. To be clear, the major semiconductor foundries continue to invest heavily in developing advanced process nodes which continue to offer improved power, performance, and area benefits. However, the economics of these advanced nodes present significant barriers to adoption for a large percentage of the ASIC / ASSP design community. Additionally, the benefits of scaling associated with migrating to the most advanced semiconductor process node do not apply uniformly across all circuit types - most notably analog circuits.

As such, while scaling through Moore’s law is still a very important vector for the semiconductor industry, the move to chiplets and the use of advanced packaging technologies are rapidly becoming an important vector for the industry - and in fact, hold the promise of spawning a new industry where chiplets from different vendors can be readily interconnected via UCIe to rapidly build new products that combine best in class technologies into a single package delivering fast time to market, low risk, and low development cost. The benefits of chiplets are quite meaningful enabling the following benefits:

• Ability to build SoCs that are larger than the reticle of the semiconductor manufacturing equipment
• Building SoCs with higher yield and correspondingly lower cost vs. monolithic solutions
• Reduced R&D cost vs. employing advanced nodes
• Accelerated time to market
• Smaller footprint - vs. discrete two-chip solution
• Higher performance vs. discrete two-chip solution
• Lower power vs. discrete two-chip solution 

UCIe is an open specification that defines the interconnect between chiplets within a package, enabling the formation of a chiplet ecosystem with a common interconnect footprint at the package level.  In effect, through industry standardization, the equivalent of a common footprint, similar to Lego®, is being established.  The UCIe standard is endorsed by 72 contributing members, and 26 adopting members at the time of this writing.  

The initial focus of the UCIe 1.0 specification was in the following areas   

• Physical Layer: Die-to-Die I/O with industry-leading Key Performance Indicators (KPIs)
• Protocol: CXL/PCIe for near-term volume attach
• Well-defined specification: ensure interoperability & evolution

The UCIe physical layer supports I/Os that can provide up to 32 Giga Transfers per second (GTs) with 16 to 64 lanes and uses a 256-byte Flow Control Unit for data, similar to PCIe 6.0.  The protocol layer is based on Compute Express Link (CXL).  In short, UCIe is leveraging tried and tested technologies that have a strong legacy of adoption across many markets.

As a testament to the importance of UCIe to the automotive industry, In August of 2023, the UCIe consortia introduced a 1.1 version of the specification with a focus on the following areas:

• Enhancement for automotive - including runtime health monitoring and repair for high-reliability applications
• New usages for streaming protocols with full UCIe stack
• Protocol support with end-to-end link layer functionality
• Cost optimization for advanced packaging resulting from new bump maps
• Enhancements for compliance testing

While all of these components of the 1.1 specification reflect a keen focus on the critical next-level details associated with chip-to-chip interconnectivity and device-to-device communication, the specific considerations for automotive applications in the UCIe 1.1 specification underscore the strong anticipated adoption of chiplets in safety-critical automotive applications. 

Addressing those safety concerns, the UCIe 1.1 version of the specification includes preventive monitoring to ensure that the die-to-die signaling  “eye” height and width are optimal and can be re-trained as needed. (As a note, the “eye” diagram is a way to measure the signal integrity of the link.  The more open the “eye” is, the greater the signal integrity).  There is also the addition of run-time testability of link health which includes the periodic parity Flit injection checking for the health monitoring of each lane with the ability to repair as required. 

The adoption of state-of-the-art technologies marks a sea change in the automotive industry where historically, automotive electronics employed mature products that were typically based on mature semiconductor process technologies.  With the advent of ADAS and AD, this has now changed. AI performance requirements that can reach PetaOPs levels for Level 5 ADAS are best addressed via heterogeneous computing solutions employing high energy-efficient, high-TOPs AI accelerators in a chiplet form factor. 

At the time of this writing, the UCIe consortia is preparing to release a 2.0 version of the specification.  When the results are made publicly available, we will take a look and see what additional features and considerations have been made to the specification and place a focus on those that specifically address the automotive market.

Learning to learn in a structured manner is the only way to maintain a long career in technology. I tell those I mentor they should settle into the reality they will likely have 5+ careers in this industry.

“Don’t be scared, just stay curious,” I say. “Do everything in your power to be the positive influence on every collaboration you engage in.”

Technology domains are small communities, and being the individual who consistently demonstrates high EIQ, dignifies vs. degrades others (regardless of the role they play) is a superpower for the long haul.

Many of us with a longer tenure in technology see patterns in industry transformations, both for good and for ill. Example: years ago, I was informed by a long tenured mainframe/UNIX expert that virtualization was a mainframe feature for decades. If a server solution was considered to be equivalent to “…a toy you find at the bottom of a cereal box”, why bother with virtualization technology that limits performance? (Extra credit: Name the CEO that gave us the soundbite comparing servers to Fruit Loops without doing a web search!)

To kick off my blog series with Tech Arena, I decided to share three patterns I have consistently seen across ~30 yrs of technology inflections.

The “best” solution in the eyes of a customer rarely offers the best raw performance.

As a hardcore engineer fresh out of college, I got my hands dirty coding some of the most complex SOCs with firmware and compiled languages. I was convinced RISC was the ‘best’ architecture. I was so excited to promote the first >100MHz System on Chip (SOC) for the ARM ISA, thinking it would be easy to sell ‘better technology.’ I instead learned about legacy code and ‘good enough’ x86/68K/PowerPC incumbency. “The best technology” may cause such an operational hassle, customers will admire, but never purchase “the best.” A company can’t maintain a profitable business on glowing reviews from technology analysts or journalists.

Corollary Lesson: Being boring, reliable, easy to debug, offering ways to sample operational data without impacting run time performance is surprisingly underrated by technology providers. To service providers, this list is REALLY important to deliver a good customer experience. Using a car analogy, a Lamborghini may be the “best” auto on a racetrack as measured by its top end speed, but when driving grade school carpools, a Lamborghini is limited to the same 25 MPH zones that a Toyota Corolla or Minivan comfortably achieves.

“What compute performance giveth, the network can taketh away.”

Maintaining optimal compute/throughput/storage is a never-ending battle. Equilibrium with enough compute power that processes data, balanced by enough storage in the right tiers, plus the right network capacity to keep the data moving – it just never lasts for long.

There are so many examples, I’ll just point you to two deeper articles to explore. Both The Register on high core count CPUs and GPUs, and Broadcomm’s article with Meta’s data on AI instance efficiency offer depth on the consequences of imbalanced systems. Now, imagine the ‘inside voice’ conversations within infrastructure operators who pay ~$1B for AI processing hardware and only see ~$500m of work done on it.

“Immutable ‘laws’ vs. Ideology and narrative”

A favorite comic in my family included something related to exercise in every routine. A classic quote is “I don’t do ups, I do DOWNS, because gravity is the law, and I obey the law!” In commercial airplanes, applied physics temporarily suspend the effects of the law of gravity, but eventually gravity wins, and the plane must land. Similarly, in every technology era, exuberance for a hot technology seems to temporarily suspend the laws of economics and the principles of supply and demand. Countless breakthroughs have promised a new utopia, to replace or better humanity, and to keep humanity from…well, being itself! The internet evangelists in 2001 claimed ‘borders didn’t matter’ and ‘information wants to be free!” Here in 2024, how many paywalls have you run into this week? Economics clapped back on utopia, because it turns out that you need to make more money than you spend to stay in business. There are a few hard lessons in the fact that customers/consumers will not spend at the level of the difficult engineering that built the product. Do consumers save more money? Do enterprises see benefits well beyond what they must spend to afford the product?

Concluding thoughts

Many of these ‘rules of thumb’ don’t need to be stated when business and technology together offer a solution that legitimately solves more problems than it causes. That often happens years after the discovery of a new technology innovation – so stay curious, keep learning and share your thoughts in the comments!

Lynn A. Comp Bio

Lynn A. Comp is a Vice President and Director of the Microsoft Global Account Team in the Intel Sales and Marketing Group. Lynn’s mission is to align the unique benefits that Intel and Microsoft offer their customers through access to unparalleled technology at scale, enabling the broadest ecosystems to innovate solutions to the most challenging business problems. Lynn returns to Intel from AMD where she served as the VP and GM of the EPYC CPU Cloud Business and the VP of EPYC Product Marketing.

Lynn has a wide range of experiences spanning her ~30 years in the tech industry, from strategic planning and go to market of RISC SOCs for both communications infrastructure and mobile phones, to software offers laying the groundwork for rapid video-based services innovation, to pioneering the foundational libraries that paved the way for ‘software defined’ networking with telecommunications operators.

Lynn has extensive experience in marketing, product management, product planning, and strategy development across software, hardware, cloud, and communications service providers (CoSPs).

Lynn has a Bachelor of Science in electrical engineering from Virginia Technology and an MBA from University of Phoenix. 

TechArena's Allyson Klein and Jeniece Wnorowski from Solidigm sit down with Kelley Mullick, Vice President Technology Advancement and Alliances, from Iceotope to discuss the latest in data center cooling technology. They dive into the role of liquid cooling in supporting AI workloads, the sustainability benefits of advanced cooling solutions, and the future of edge computing.

While no day in tech is technically slow, we’ve hit mid-summer, which means that conferences have slowed, Europeans are jetting off to holiday, and everything else is feeling a bit like wading through molasses in terms of the regular maelstrom of advancement that we’ve come accustomed to in the AI era. A day like today makes me look forward to what is on the horizon, and there is plenty to look forward to in the months ahead. Today’s focus is the OCP Summit coming up October 15-17 in San Jose, and if you have peered into the roster of industry sponsors like I have you’ve noticed that OCP has…well…super-sized.

I remember the days that this Summit would feature about three top tier sponsors vying for the spotlight, and this year features nineteen. NINETEEN. This is just the top tier players, and as you scroll through the list of companies spending major money to feature their tech at the show you realize that OCP has become the nexus of what is happening in AI infrastructure. Silicon providers, infrastructure builders, power and cooling entities, oil companies – all showing up to talk about the future of the data center. All likely engaged in OCP configuration delivery and participating in conference proceedings in a major way.

The TechArena is excited to be part of the OCP Summit this year again as a media sponsor, and we are especially excited to dip into conversations regarding the advancement of sustainable infrastructure, how the fabric of the future will be shaped including advancement of Sonic (which we covered extensively in Lisbon this April), what’s next in memory advancement, and of course power and cooling innovations required to fuel these ridiculously hungry racks. We’ll be podcasting daily, engaging with vendors in video discussions from the show floor, and writing insight stories for those unable to make it to San Jose. We’re keen to know what is on the top of your mind in regards to hyperscale infrastructure priorities as well! Please take part in our daily polls on my LinkedIn feed this week to share your views on what will grab the world’s attention during the summit in October.

“We need NASA, because we need a tower of flame 60 miles high burning at temperatures hotter than the surface of the sun.” - Nova, Sharknado III

Technology: it’s all about the sublime new features. That intricate blend of performance and programming, design and development, brilliant insights leading to giant leaps of innovation. Right? We’re talking about the best and the brightest innovators, those ultra-cool kids who opted to take all those math classes, knowing that the Psychology majors were already hitting quarter beer night while they wrote up their fourth page of derivatives. But it was all worth it, because someday…some wonderful day…the world would recognize that Linear Algebra would make things safe for, um, whatever they’d get paid a lot to algebrate.

As cool as technology is, the reality of any good high-tech solution involves more boring logistics than elegant engineering. For all the results, the hours of sweat are more about jamming six new ideas in a space that holds three and hoping that something doesn’t break once the test suite is finished. Innovation is built on the shoulders of a lot of swearing in small rooms with smudged whiteboards. And maybe the archaeology of those smudges reminds some about how it was done before.

The modern data center began sometime in the early ‘00s, when commodity servers began to fill the racks, displacing RISC machines. (Sometime later, we’ll talk about how it was a stock market bust that revolutionized the datacenter.) Virtualization hadn’t hit volume yet, so most servers were single-task machines. The really innovative IT organizations were just starting to think at a rack level with stateless transactions.

The move from computer rooms to data centers was only twenty years old or so. The common architecture was a raised floor that delivered power and pulled air downward (it was fun to watch the airflow using the cigarette smoke back in the day). We were moving on from a legacy of big servers that filled a tile or two and pulled in the range of 6-8kW of power, and now facing a world where evolving commodity servers in a rack were - gasp - demanding 10kW, maybe 12kW, per tile. This was also when the data center was generally located in prime real-estate on the buffer floor in between IT and management, so forgive us for the infrastructure costs back then. We thought local, not global.

The conversations back then went to two simple vectors. One: make servers with less power. It would take a bit for us to figure out how to turn off portions of silicon that weren’t doing anything, and performance dictated that we’d probably claim that power back anyway. Two, and usually prevalent: brute-force the data center to adapt. In some cases, that meant that a rack or two of servers on a tile would be isolated by a few blank tiles to keep up with the limited cooling capacity in the room. It was not entirely uncommon to find a box fan or two on top of a rack to shove air over to another air intake.

The next innovations focused on logistics, mechanics, and air flow over server engineering. The data center began to evolve as the cloud companies led the charge to dedicated buildings with specific architecture to optimize power delivery and air movement. Hot/cold aisles started to appear, as did plenums and upward air flow. That alone bought a few kW of breathing room, even as the racks began to evolve to support a mix of compute, network, and storage to handle virtualized workloads. These days, it’s not uncommon to see 20kW racks in a vanilla data center, standing on those shoulders of innovation.

Of course, the demands never end. The commodity server isn’t as cool (pun intended) as it used to be, we processor folks blame the memory. And the rise of dedicated GPU workloads is back to driving power of individual rack units to new heights. It’s not uncommon for a full rack of AI/ML servers to demand up to 60kW of power. As before, the brute-force solutions appear to be back in vogue. Depopulating space to support the boutique solutions is back, minus the box fans. We don’t allow humans in the data center anymore, they’re just wasted heat. It’s likely that commodity will start to pull in AI innovation, and a bit of re-architecture will save some power, but the end result is likely the same. It’s likely time for another innovation in the actual buildings to accommodate the new rack reality.

It’s not hard to imagine another twenty years when we’re all chuckling about those old days where 50kW created a week of meetings in the conference room, and maybe the AI can peer back through the smudges to realize the old solutions might be useful again. So for now, here’s to the next generation of geeks skipping out on the vape party to do homework. We’ll need you then like we always did.

The world is rapidly transforming with new applications of AI across numerous industries. I’ve been fascinated by the advancement of AI in the world of pharma research in particular, given the complex process to identify an early drug candidate, conduct preclinical research and run phases of clinical trials before reaching mass market introduction.

We recently discussed pharma within our paper on semiconductor requirements for AI advancement. I am reminded of this quote from Oii’s CEO and founder Bob Rogers:

“For each application area in drug development, the speedups reported by AI vendors are 3x to 10x. These accelerations by themselves are significant, but the real magic lies in the fact that every step of the drug development process is currently built from interlocking, inefficient human tasks. Replacement with AI tooling will result in wholesale reductions in the time it takes to propose, test, and report on new drugs in the market.” 

So where is the low-hanging fruit? I’d argue that one is the world of microscopy, where early stage research is performed under electron microscopes to seek information about the chemical structures of compounds for potential efficacy to target disease. This stage of the process is incredibly human intensive with work that – frankly – can be done faster and with more precision by AI visualization techniques.

Enter Health Technology Innovations, Inc. (HTI), a National Science Foundation-funded AI operation that is targeting the space with collaborations with the leading microscope vendors and research institutions. Led by industry veteran Tuan Phamdo, HTI has built a comprehensive Cryo-FAST software platform that helps accelerate pharma research. According to the company’s claims, HTI has driven average screening time from six hours to 5 minutes, halved scope collection time, and cut annual lab costs for this function by 50%. Today, the folks at HTI announced the fantastic news that their software is ready for mass market adoption as a 1.0 offering.

This advancement has gotten the attention of large players in the sector. Gatan, JEOL, and Thermo Fischer – all major players in electron microscopy – have collaborated with HTI on the delivery of this solution. The Northwest Cryo-EM center at Oregon Health Sciences University have also deeply engaged, and I’d expect to start seeing major research labs deploying HTI’s software in tight integration with their scope of choice. 

What’s the TechArena take? Well, we’ll go where this blog started. AI is a massive disruptive force, and solutions like HTI’s will help advance scientific discovery at record pace. We’re excited to see this as a proof point of the larger transformation happening in pharma and can’t wait to see societal benefits from solution adoption. We also expect HTI to become much better known quickly in the world of pharma research as a player that is delivering real technology to customers with the help and support of industry leaders. We can’t wait to hear more about solution deployments in the months ahead.

TechArena host Allyson Klein chats with Eric Dahlen from Intel and Alex Rakow from Schneider Electric about their roles in the Compute Sustainability group of the Open Compute Project. They discuss AI's impact on data center sustainability, power and cooling innovations, and the upcoming OCP Summit.

The pace of innovation in AI is moving at light speed, so it was only a small surprise recently when Antti Karjalainen shared his company’s vision beyond large language models (LLMs). 

“So what is an agent? It's a piece of software that, contrasting to traditional software that we've been used to, does not just make you more efficient at your work. Agents will actually complete the work for you. So think about them as sort of a knowledge worker or software that can reason, collaborate and act with humans.”

Antti went on to explain that what we’ve seen with generative AI is just the beginning, and it’s in the application of these models, in his view with agents, that the real transformation of our industries and society will start taking shape.

What Sema4 is delivering is fascinating in terms of its broad reach across industries and the disruptive productivity it places in the hands of human collaborators. In Sema4’s vision, each individual can be unleashed with unlimited AI workers that can research, reason, and deliver task work, freeing the human for more value-added work. At first blush, this vision looks similar to Microsoft co-Pilot. But upon further analysis, it’s more optimized for unique industries or business processes. Antti explained the difference, being that “the AI agent is something that you can actually interact with to get a full work product done.” 

“Instead of just assisting on the side, an AI agent is kind of the main thing. An AI agent is going to be a new way to interact with enterprise applications, whereas a co-pilot is more of an additive layer on top,” he said.

When you consider the autonomy in this statement, you can start understanding the power that agents represent to autonomous control of digital functions holistically and start imagining the complexity of delivering this level of control to the enterprise. The team certainly has the technology chops and enterprise awareness to deliver the goods with far-ranging backgrounds from tech leaders including AWS, Cloudera, Docker, HortonWorks and more. Today, the Sema4 team is working with a range of business functions including customer support and finance operations to deploy initial agents into enterprise environments. Tomorrow, Antti described additional functions including HR and software development as part of a near-term vision for deployment.

So what about guardrails? Sema4 is tackling security as well as data integrity flowing into training agents as keys to success. Layer on top of that the organizational compliance and enterprise-class resilience of model adoption, and you can understand that the human trust in bringing AI agents into the enterprise is likely the main time investment in this powerful technology transition. Antti expects to see a wealth of use case deployment examples emerge in the second half of 2024 as the first signal of broad agent adoption.

What’s the TechArena take? We aren’t surprised anymore by the speed of innovation being driven by AI. IT organizations are being pushed to their limits to be agents of change, no pun intended, within the enterprise, and we expect business functions to directly adopt agents with or without internal IT support – much as they did with the initial activation of cloud services to fuel business agility. 2024 and 2025 will be rife with stories of success in this arena, and we can’t wait to hear them. As to worker response to agent deployment…we’ll leave that topic for an upcoming blog as it’s a lot to unpack and urgently needs to be discussed in order to get full value out of these powerful tools.

Advanced Driver Assistance Systems (ADAS) are all the rage. Increasingly, consumer car purchasing decisions are based on ADAS features as compared to vehicle style, engine size, or branding, which historically has been the case.

A quick internet search will provide one with an obligatory understanding of the capabilities associated with each ADAS level. A high-level summary of the different ADAS levels is as follows:

Level 0: No Driving Automation

Level 1 - Driver Assistance

Level 2 - Partial Driving Automation

Level 3 - Conditional Driving Automation

Level 4 - High Driving Automation

Level 5 - Full Driving Automation

What is profound, at least in my mind, is that there is no consistency regarding what specific ADAS capabilities are associated with a given ADAS level. Furthermore, the industry has introduced yet a new level called L3+. Not quite High Driving Automation but approaching autonomy. Apparently, good enough isn’t good enough.

The general goal of ADAS is to improve the overall safety of the driving experience. To keep this tone upbeat, I won’t focus on the mortality rate associated with driving, however, one key point to note is that it is estimated that 97% of all auto accidents are due to driver negligence. The key takeaway is that most accidents can be easily avoided through the use of technology, which is what ADAS brings to the table.

At the lower ADAS levels, the goal is to provide the driver with safety assistance features so the driver is more aware of their surroundings and can respond to the environment appropriately. Features like blind spot detection or backup cameras, typically considered Level 1/ Level 2 don’t have any physical impact on the operation of the vehicle itself but provide valuable insights to the driver to avoid what may have otherwise been a significant incident.

The progression from Level 2 to Level 3 and beyond is significant in terms of the level of electronic content that is employed to actively prevent accidents leading to the point where, at levels 4 and 5, the vehicle is driving autonomously without some level or any level of human intervention.

The impact in the reduction of accidents associated with each progressive ADAS level along with the benefits of freedom that comes from emancipating the need for a driver are profound. These benefits range from enabling a “greener” relationship between our cars and the environment to providing mobility to those who otherwise might be immobile.

The technology that is required to pull off these increasing levels of ADAS is pushing the state-of-the-art in every category. The overused phrase “data center on wheels” is an understatement regarding the level of complexity and technology that is under the hood of the modern car. Achieving level 3 ADAS typically requires 18 or more high-resolution cameras in addition to a dozen radar sensors and typically a LIDAR (light detection and range) sensor. The underlying computing engine that processes these massive amounts of sensor data typically employs AI computing that delivers 100s of Trillion - Tera Operations Per Second (TOPs).

To date, few passenger vehicles have been certified to be Level 3 compliant. And yet, Level 3 still requires a driver to be present and ready to take over control of the vehicle in the case where the capabilities of the electronics are exceeded in their ability to control the vehicle – a far cry from the vision of the self-driving car. Achieving Level 3 ADAS and beyond is pushing semiconductor process technology limits – TSMC has announced an automotive-qualified 3 nm process technology expressly to address the demands of this market as the number of transistors and their associated thermal footprint have become meaningful. One of the hottest technologies – chiplets – is also being readily embraced by the automotive market to most effectively address the mismatch in technologies required to achieve the vision of the self-driving car.

The emerging industry standard UCIe™ (Universal Chiplet Interconnect Express™) announced a 1.1 version of the specification expressly to address the automotive application space focusing on areas such as data integrity and reliability. More about chiplets and the UCIe specification will be discussed in future blogs. In short, outside of quantum computing, it seems like the automotive market is taking a leading role in defining the future of many of the emerging technologies and is a good reason to follow the exciting innovations that the automotive market is now “driving.”

Cornelis Networks shot a huge salvo today into the fabric community with the announcement of Intel veteran Lisa Spelman assuming the CEO seat for the startup. Rumblings started on the Internet yesterday that a major Intel executive was leaving the firm for a new home. Spelman was an Intel veteran, having served long-standing leadership roles in the data center and AI group as well as leading the company’s Xeon processor strategy. She’s been widely hailed as a face to watch for higher levels of corporate leadership, making this departure yet another strategic talent exit for the chip giant.

What does this mean for Cornelis? The company has a history of leadership in the HPC arena, having delivered Omni-Path fabrics to data centers and having delivered a robust portfolio of high speed fabric solutions. For those who may remember, Omni-Path was originally designed within the walls of Intel before being re-born as an independent company four years ago. And while HPC fabrics are interesting, the strategic value of Cornelis, and likely the reason why Spelman was interested in driving this tech further into the market, is AI’s insatiable fabric demand. We’ve written about the need for a fabric alternative to NVIDIA’s InfiniBand solutions on the TechArena at length before, and the Ultra Ethernet consortium is certainly building a groundswell of momentum for alternative solutions for AI clusters.

If you’d asked me yesterday who was going to lead new fabric innovation for AI, I likely wouldn’t have Cornelis on the top of my list. But the addition of Spelman, and her deep knowledge of the AI landscape, incredible relationships with ecosystem and customers built from her time driving Xeon processors, and frankly business savvy in terms of what it takes to create categories and grow tech leadership with customers, Cornelis has placed themselves squarely in the mix. I’m interested to learn more about how the Omni-Path based IP mixes with AI customer requirements, how Lisa will place a different focus on delivery of tech from previous leadership, and how strategic collaborations will be brought to bear to scale Cornelis’s ascent in the marketplace. Watch this space for more information in the weeks ahead as Cornelis begins to unveil it’s expected evolution.

TechArena host Allyson Klein chats with Sema4.ai co-founder Antti Karjalainen about his vision for AI agents and how he sees these powerful tools surpassing even what current AI models deliver today.

In our latest TechArena Data Insights interview, Jeniece Wnorowski and I had the pleasure of chatting with Doug Emby, Vice President of Cheetah RAID. We delved into the fascinating world of cutting-edge storage solutions tailored for edge environments, which are crucial for industries such as entertainment, defense, and autonomous vehicles.

Doug shared insights about the remarkable Cheetah RAID Raptor and Prowler servers. These servers are designed to handle the rigorous demands of media and entertainment as well as military applications. The Raptor 2U server, in particular, is a powerhouse with its high storage capacity, robust performance, and rugged design. It boasts up to 737.28TB (using Solidigm D5-P5336 61.44 SSD) of storage capacity, hot-swappable NVMe canisters, and support for PCIe Gen4, ensuring rapid data transfer and reliable performance even in extreme conditions. You can imagine how these hot-swappable canisters could be used to quickly capture data in rugged environments and transport it swiftly at the end of a shoot day or in a migrating environment. Given the reliability of SSD technology, this use case works effortlessly without significant risk of data loss or drive degradation.

How much data can these solutions handle? A key part of our discussion was about the importance of scalable storage solutions for managing vast amounts of data at the edge. The Solidigm D5-P5336 SSD, which is integrated into Cheetah RAID’s systems, stood out for its high capacity and performance. This SSD is optimized for data-intensive workloads, including AI-driven data lakes, big data analytics, and scale-out NAS, providing efficient and rapid storage and retrieval of extensive datasets. Jeniece shared some insights on how Solidigm’s SSD technology is integral to these advancements. She highlighted features such as enhanced power loss data protection, hardware encryption, and temperature monitoring, all of which are essential for maintaining data integrity and performance in various edge applications.

One of the most compelling parts of our conversation was understanding the synergistic innovation driven between the two companies. Their collaboration has resulted in powerful and efficient storage solutions that are being relied on by IT leaders across industries. These servers ensure that critical data is stored securely and accessed quickly when needed, making a significant impact in real-world scenarios. Cheetah RAID’s high-performance server/storage, featuring hot-swap drive canisters and Solidigm’s 61.44TB drives, makes a powerful statement unmatched by others in the market.

For those interested in the technical details and practical applications of these innovations, the full interview provides a wealth of information on the future of scalable storage solutions at the edge. To dive deeper into our discussion, you can visit the TechArena interview here and learn more about Cheetah RAID's innovative products on their official page here.