When To Expect Domain-Specific AI Chips

Sigasi CEO Weighs in on AI and Domain-Specific Computing

Semiconductor Engineering’s Brian Bailey put together an intriguing think piece about domain-specific silicon and AI. Throughout, he presents thoughts from industry leaders like Quadric, Blue Cheetah, Arteris, Keysight, Siemens EDA, Expedera, Mythic, and Ansys. And also Sigasi: “When it comes to software tasks like LLMs, they will become dominant enough to force new hardware architectures, but won’t stop differentiation all together, not in the short term,” says Dieter Therssen, CEO of Sigasi. “Even the customization of RISC-V is based on the need to do some CNN or LLM processing. A key factor here will be how AI is deployed. Currently, there are so many ways to do so that imaging convergence is still too far out.”

From the Article

The chip industry is moving toward domain-specific computation, while artificial intelligence (AI) is moving in the opposite direction, creating a gap that could force significant changes in how chips and systems are architected in the future.

Behind this split is the amount of time it takes to design hardware and software. In the 18 months since ChatGPT was launched on the world, there has been a flood of software startups exploring new architectures and technologies. That trend is likely to continue given the rate of change for tasks being mapped onto them. But it often takes longer than 18 months to produce a single customized chip.

In a world of standards, where software does not change much over time, it pays to customize hardware to meet the exact needs of an application or workload, and little else. This is one of the major drivers behind RISC-V, where the processor ISA can be designed specifically for a given task. But with many flavors of AI, changes are so rapid that hardware already may be outdated by the time it reaches volume manufacturing. So hardware that is specifically optimized for an application is unlikely to reach the market quickly enough to be useful unless the specification is constantly updated.

As a result, the risk that a domain-specific AI chip will not work correctly on the first pass increases. And while it is being fixed, generative AI will have moved on.

Click here to read the full article at Semiconductor Engineering. 

2024-08-16, last modified on 2024-09-25