New chip architectures for today’s AI

#News(General) [ via IoTForIndiaGroup ]

It seems that AI is now shifting into the hardware realm, specifically in the development of integrated circuits (ICs). I spoke recently with Nagendra Nagaraja and Prashant Trivedi, two of the founders of a deep technology start-up called AlphaICs, who are trying to revolutionize the design of ICs to meet AI’s future needs. Vinod Dham, the reputed designer of some of Intel Corp.’s breakthrough chips, such as the Intel Pentium, is the third founder.

Of the three mainstream hardware platforms—Intel and other CPU chips popular in laptops and servers, ARM chips in mobile devices, and high-performance gaming chips called GPUs, mostly from Nvidia—GPUs seem to have the edge today in AI development. This is because today’s CPUs are primarily scalar-based, wherein a single Instruction operates on a single piece of data, and GPUs are vector-based, wherein a single instruction operates on a “linear array” of data called vectors, and Nvidia has capitalized on the opportunity. Nagaraja was a chip designer at Nvidia.
AlphaICs claims to have built a custom hardware platform for “supervised” self-learning agents that are delivering “reinforcement” learning today and will provide the foundation for unsupervised learning in the future as AI evolves, in a process they call “Real AI”.

The AlphaICs Real AI Processor (called RAPTM), is based on “agents”, a group of interconnected “tensors” (mathematical objects analogous to but more general than the vectors I have referenced above that are found in GPUs). Nagaraja claims that today’s GPUs do not have the architecture to handle a divergence of threads needed for “reinforcement” learning, while “agents” do.

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