From DSPs to artificial intelligence

May 30, 2019 // By Andrew Richards, Codeplay Software Ltd.
From DSPs to artificial intelligence
Richards, co-founder and CEO of Codeplay Software Ltd. (Edinburgh, Scotland), discusses the movement of artificial intelligence software from DSPs and graphics processors, which are hosts to much AI software today, to other platforms.

Huge claims are being made about what AI will deliver to the world, from self-driving cars to virtual assistants. But delivering on these promises is more work than the hype might suggest.

One of the biggest challenges is delivering the huge amount of processing power AI requires, but that challenge is also an opportunity for chip companies to create the AI processors of the future. Get it right and a chip company can make the x86 of the AI future. Get it wrong and that chip company can make the Itanium of the AI future. Software has the largest influence on AI system performance: if a chip company can accelerate the world’s AI software with their processor then they win big, but if they accelerate only a few AI demos then their processor will be the interesting curiosity that never succeeds in the real world.

Right now, the incumbent AI processor is made by NVIDIA. Their GPUs (graphics processing units) power almost all the world’s AI software today. Currently, the only significant competitors to NVIDIA are closed proprietary systems that provide both the AI software and AI processor, such as Google’s TPU or Intel’s Mobileye. NVIDIA, with extensive researchers and partners around the world, has grown a huge ecosystem of AI frameworks, tools and components making it easy for developers to quickly build AI software that is tied to NVIDIA’s GPUs.

But what if developers and manufacturers want to transition to non-NVIDIA GPUs? Can we create an open ecosystem and community that provides wider support to avoid the NVIDIA lock-in? Developers want an ecosystem that enables any AI processor to accelerate their application, and that is widely used by AI researchers and software developers. This ecosystem would allow developers to write AI software then benchmark and profile it on many AI processors. Ultimately, they will end up with the best AI processor for the job.

My background is not from AI, but instead from videogames development. Strangely, a huge amount of AI technology comes indirectly out of videogames technology, such as NVIDIA GPUs, designed for videogames graphics and programmed in C++. But at the same time, AI uses it in a subtly different way. To make the incredible smoothly-animated and life-like graphics expected of today’s videogames, game developers have developed a range of techniques to deliver unparalleled high levels of processing performance, while also being creative and original. It is this combination of high performance and creativity that has made GPUs such a great enabler for AI researchers.

But the videogames industry isn’t the only industry that relies on very high-performance processors. The DSPs (Digital Signal Processors) that enable today’s super-fast internet and mobile networks also provide incredible levels of processing power. At first, AI looks more like digital signal processing than like videogames. Both DSPs and AI take in data, pass them through a series of mathematical operators and output processed results. So why is it that the AI industry has grown out of videogame graphics and not out of digital signal processing? More AI processors being designed today are based on DSPs, not GPUs, yet the AI industry overwhelmingly sticks to GPUs. There is something strange going on in AI that needs to be understood. We must learn from the videogames industry and apply that experience to enable us to open up the AI accelerator market.

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