Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing

Abstract The effort addresses the research activity around the usage of non-volatile memories (NVM) for storage of ‘weights’ in neural networks and the resulting computation through these memory crossbars. In particular, we focus on the CMOS implementations of, and comparisons between, memristor/resistive random access memory (RRAM) devices, and floating-gate (FG) devices. A historical perspective for illustrating FG and memristor/RRAM devices enables comparison of nonvolatile storage (addressing issues related to resolution, lifetime, endurance etc), feedforward computation (different variants of vector matrix multiplication, tradeoffs between power dissipation and signal to noise ratio etc), programming (addressing issues of selectivity, peripheral circuits, charge trapping etc), and learning algorithms (continuous time LMS or batch update), in these systems. We believe this historical perspective is necessary and timely given the increasing interest in using computation in memory with NVM for a wide variety of memory bound applications.