Mixed-Signal POp/J Computing with Nonvolatile Memories

The present-day revolution in deep learning was triggered not by any significant algorithm breakthrough, but by the use of more powerful GPU hardware [1]. Though this revolution has stimulated the development of even more powerful dedicated digital systems [2, 3], their speed and energy efficiency are still insufficient for ultrafast pattern classification and more ambitious cognitive tasks. The main reason is that the use of digital operations for the implementation of neuromorphic networks, with their high redundancy and noise/variability tolerance, is inherently unnatural. On the other hand, the network performance may be dramatically improved using mixed-signal integrated circuits, where the key inference-stage operation, the vector-by-matrix multiplication, is implemented on the physical level by utilization of the fundamental Ohm and Kirchhoff laws [4-6].