AI Hardware

introductory video - The AI Hardware Problem

Mapping the 100bn IT spending opportunity
2018 Big Bets on A.I. Open a New Frontier for Chip Start-Ups, Too

advanced discussion Future of AI Hardware Panel Dave Patterson, Bryan Catanzaro, Andrew Feldman, & Cade Metz

technical Does AI have a hardware problem?

As deep neural networks continue to improve and grow, innovations in hardware will be required in order to meet the increasing computational demands.

2018 +++ Scaling for edge inference of deep neural networks Xiaowei Xu

page 218, see references 50-56

Recently, representative array-level demonstrations have been reported. These include IBM’s 500 × 661 phase change memory array for handwritten-digit recognition using the Modified National Institute of Standards and Technology (MNIST) database (52), Tsinghua’s 128 × 8 analogue resistive RAM array for face recognition (53), UCSB’s 12 × 12 crossbar array for pattern recognition (54), and UCSB’s floating-gate array for MNIST image recognition (55)

2017 +++ Can Li Analogue signal and image processing with large memristor crossbars R. Stanley Williams

The energy efficiency of the system was over 119.7 trillion equivalent operations per second per watt using
a readout of 10ns, and this is expected to increase significantly with larger vectors and matrices and with improvements in circuitry