Analog Neurocomputing with Emerging Memory Devices

The revolution in deep learning was triggered not by any significant algorithm breakthrough, but by the use of more powerful GPU hardware. Though this revolution has stimulated the development of even more powerful digital circuits, their speed and energy efficiency is still inadequate for more ambitious cognitive tasks. On the other hand, the network performance may be dramatically improved using mixed-signal integrated circuits based on emerging nonvolatile memories, where the key inference-stage operation, the vector-by-matrix multiplication, is implemented on the physical level by utilization of the fundamental physical laws. I will review the recent progress of such mixed-signal neuromorphic networks based on floating-gate memories and metal-oxide memristive arrays. The recent experiment results for 180-nm NOR flash pattern classifier showed >×10³ improvements in propagation delay and energy dissipation per inference operation compared to digital implementation. The performance can be further improved by utilizing memrisistors, which are scalable to 10-nm and suitable for 3D integration. Thier fabrication technology was recently improved to demonstrate the first simple integrated neuromorphic networks.