AI Hardware: Harder Than It Looks

The second AI HW Summit took place in the heart of Silicon Valley on September 17-18, with nearly fifty speakers presenting to over 500 attendees (almost twice the size of last year's inaugural audience). While I cannot possibly cover all the interesting companies on display in a short blog, there are a few observations I'd like to share. Computer architecture legend John Hennessy, Chairman of Alphabet and former President of Stanford University, set the stage for the event by describing how historical semiconductor trends, including the untimely demise of Moore's Law and Dennard scaling, led to the demand and opportunity for "Domain-Specific Architectures." This "DSA" concept applies not only to novel hardware designs but to the new software architecture of deep neural networks. The challenge is to create and train massive neural networks and then optimize those networks to run efficiently on a DSA, be it a CPU, GPU, TPU, ASIC, FPGA or ACAP, for "inference" processing of new input data.