There’s no doubt that AI is driving every agenda – whether it’s hardware, software, automation, or anything else. This was clear at last week’s ‘AI Chrysalis Chronicles’ panel discussion at the end of the Silicon Catalyst Spring 2025 Portfolio Company Update at the Computer History Museum, in Mountain View, CA – where the panelists debated the energy demands posed by AI, but how they were optimistic that silicon innovation would come to the rescue.

But in what form will that rescue come? Well, here in Antwerp, Belgium, this week, the CEO of imec, Luc Van den hove, will tell the ITF World 2025 conference that AI’s future hinges on hardware innovation, and that could very likely come in the form of programmable AI silicon. He will explain that as we head towards agentic and physical AI, hardware is going to struggle to handle the diverse workloads in a ‘performant and sustainable way’. And the problem is that developing dedicated AI hardware takes significantly more time than writing algorithms, and the software guys will then have superseded the hardware guys by the time silicon comes out.

“This is because agentic AI, which focuses on decision-making and is highly relevant for medical applications, and physical AI, which focuses on emphasizing embodiment and interaction with the physical world for robotics and autonomous cars, require a myriad of different models. Each model serves a specific purpose and interacts with the others, forming an AI system that can combine large language models, perception models, and action models. Some models require CPUs, some GPUs, and others are currently lacking the right processors.”

Quoting from his blog, Van den hove said, “To prevent bottlenecks from slowing down next-gen AI, we must reinvent the way we do hardware innovation.”

The challenge is that while simply adding brute compute power and data has done an excellent job for the first generation of large language models (LLMs), the transformation of generative AI toward reasoning models and workloads will become increasingly heterogeneous, and a one-size fits all approach using just brute force compute may not necessarily be the right solution to deal with a chain of various workloads

From EETimes