Ty Garibay, CTO at Arteris IP, authored this Semiconductor Engineering article:
Artificial Intelligence Chips: Past, Present and Future
August 2nd, 2018 - By Ty Garibay
It's been an uneven path leading to the current state of AI, and there's still a log of work ahead.
Artificial Intelligence (AI) is much in the news these days. AI is making medical diagnoses, synthesizing new chemicals, identifying the faces of criminals in a huge crowd, driving cars, and even creating new works of art. Sometimes it seems as if there is nothing that AI cannot do and that we will all soon be out of our jobs, watching the AIs do everything for us.
One of the key challenges in AI chip design is putting it all together. We are talking here about very large custom systems-on-chip (SoCs) in which deep learning is implemented using many types of hardware accelerators. Designing AI chips can be very difficult stuff, especially given the rigorous safety and reliability demands of the automobile industry, but AI chips are still just chips, perhaps with some new solutions in terms of processing, memory, I/O, and interconnect technologies. Companies like Google and Tesla, which are new to IC design, as well as AI chip upstarts such as AIMotive and Horizon Robotics, bring deep knowledge of the computational complexities of deep learning, but they may face serious challenges developing these state-of-the-art SoCs. Configurable interconnect IP can play a key role in ensuring that all of these new players in the industry will be able to get to functional silicon as quickly as possible.
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