The Next Frontier of AI: Key Insights from AMD’s Salil Raje
While at Mobile World Congress (MWC) in Barcelona, I had the delightful opportunity to chat with Salil Raje, senior vice president and general manager of Adaptive and Embedded Group within AMD.
During our fireside chat, Salil shared some exciting insights about the future of AI at the edge—one of the most transformative trends in technology today.
Here are five key takeaways from Salil’s conversation at MWC.
1. AI at the Edge: A Paradigm Shift
While AI’s rise in the cloud captured attention in 2024, Salil anticipates that things will shape up differently in 2025, with the real transformation lying at the edge.
“AI will be everywhere—from satellites to devices,” he said.
As AI moves closer to users and devices, it enables real-time data processing, reducing latency and improving decision-making. This shift is crucial for industries like healthcare and automotive, where speed and efficiency are vital.
2. The Power of Federated Learning
Federated learning is a game-changing technology that powers edge AI. It allows devices to process data locally and send only necessary updates to the cloud, where the model weights are coalesced and then sent back to the edge. This minimizes data transfer and improves decision-making speed.
3. Revolutionizing the Automotive Industry
In the automotive sector, AI at the edge is transforming not just autonomous driving, but also safety and driver experience. Salil mentioned AMD’s partnership with Subaru, which centers on reducing fatalities to zero by 2030. Through AI, Subaru’s safety systems are becoming more intelligent, processing real-time data for faster decision-making. Additionally, AI companions are enhancing the driving experience, personalizing interactions and improving safety.
4. AI in Healthcare: Beyond Diagnosis
AI’s impact on healthcare is already irrefutable, but Salil highlighted how AI and robotics are taking things a step further. AI-driven exoskeletons, for example, are helping individuals who have lost a limb regain mobility and functionality, offering a new level of independence and improving their quality of life. AMD’s partnership with Hiroshima University utilizes AI to improve rates of early detection of cancer, while their partnership with Clarius aims to enhance diagnostics through advanced portable ultrasound imaging techniques. These innovations show how AI at the edge is improving not just diagnostics, but patient care in real time.
5. The Silicon Behind the Revolution
To support AI at the edge, powerful, specialized hardware is essential. Unlike cloud AI, which relies on massive CPUs and GPUs, edge devices require more compact and efficient solutions. Salil highlighted AMD’s innovative hardware, such as the Versal AI Gen 2, which integrates CPUs, GPUs, and FPGAs into a single platform designed for edge workloads. This hardware helps industries efficiently process complex data while meeting size, power, and cost requirements.
What’s Next for AI at the Edge?
The potential for AI at the edge is vast, but there are still hurdles to overcome. For example, Salil pointed out the need for faster adoption within telecom, a sector that has been slower to deploy AI.
So, what’s the TechArena take? In the coming years, more industries will embrace AI at the edge, enabling smarter systems and better, faster decision-making. As Salil put it, we’re on the brink of an “AI moment” that will reshape the way we interact with technology.
While at Mobile World Congress (MWC) in Barcelona, I had the delightful opportunity to chat with Salil Raje, senior vice president and general manager of Adaptive and Embedded Group within AMD.
During our fireside chat, Salil shared some exciting insights about the future of AI at the edge—one of the most transformative trends in technology today.
Here are five key takeaways from Salil’s conversation at MWC.
1. AI at the Edge: A Paradigm Shift
While AI’s rise in the cloud captured attention in 2024, Salil anticipates that things will shape up differently in 2025, with the real transformation lying at the edge.
“AI will be everywhere—from satellites to devices,” he said.
As AI moves closer to users and devices, it enables real-time data processing, reducing latency and improving decision-making. This shift is crucial for industries like healthcare and automotive, where speed and efficiency are vital.
2. The Power of Federated Learning
Federated learning is a game-changing technology that powers edge AI. It allows devices to process data locally and send only necessary updates to the cloud, where the model weights are coalesced and then sent back to the edge. This minimizes data transfer and improves decision-making speed.
3. Revolutionizing the Automotive Industry
In the automotive sector, AI at the edge is transforming not just autonomous driving, but also safety and driver experience. Salil mentioned AMD’s partnership with Subaru, which centers on reducing fatalities to zero by 2030. Through AI, Subaru’s safety systems are becoming more intelligent, processing real-time data for faster decision-making. Additionally, AI companions are enhancing the driving experience, personalizing interactions and improving safety.
4. AI in Healthcare: Beyond Diagnosis
AI’s impact on healthcare is already irrefutable, but Salil highlighted how AI and robotics are taking things a step further. AI-driven exoskeletons, for example, are helping individuals who have lost a limb regain mobility and functionality, offering a new level of independence and improving their quality of life. AMD’s partnership with Hiroshima University utilizes AI to improve rates of early detection of cancer, while their partnership with Clarius aims to enhance diagnostics through advanced portable ultrasound imaging techniques. These innovations show how AI at the edge is improving not just diagnostics, but patient care in real time.
5. The Silicon Behind the Revolution
To support AI at the edge, powerful, specialized hardware is essential. Unlike cloud AI, which relies on massive CPUs and GPUs, edge devices require more compact and efficient solutions. Salil highlighted AMD’s innovative hardware, such as the Versal AI Gen 2, which integrates CPUs, GPUs, and FPGAs into a single platform designed for edge workloads. This hardware helps industries efficiently process complex data while meeting size, power, and cost requirements.
What’s Next for AI at the Edge?
The potential for AI at the edge is vast, but there are still hurdles to overcome. For example, Salil pointed out the need for faster adoption within telecom, a sector that has been slower to deploy AI.
So, what’s the TechArena take? In the coming years, more industries will embrace AI at the edge, enabling smarter systems and better, faster decision-making. As Salil put it, we’re on the brink of an “AI moment” that will reshape the way we interact with technology.
