Comparative Evaluation of Memory Technologies for Synaptic Crossbar Arrays -- Part I: Robustness-driven Device-Circuit Co-Design and System Implications
Chunguang Wang, Jeffry Victor, and Sumeet K. Gupta

TL;DR
This paper compares four memory technologies for synaptic crossbar arrays in in-memory computing, focusing on robustness and accuracy in DNNs, and finds FeFETs offer the best resilience and accuracy among them.
Contribution
It provides a comprehensive design space exploration and fair comparison of CMOS and post-CMOS technologies for synaptic crossbars in IMC systems, including device-circuit co-optimization.
Findings
FeFETs achieve highest DNN accuracy and robustness
ReRAMs closely follow FeFETs in performance
Device-circuit co-optimization enhances system robustness
Abstract
In-memory computing (IMC) utilizing synaptic crossbar arrays is promising for energy-efficient deep neural network (DNN) accelerators. Various technologies (CMOS and post-CMOS) have been explored as synaptic device candidates, each with its own pros and cons. In this work, we perform a design space exploration and comparative evaluation of four technologies viz. 8T SRAMs, ferroelectric transistors (FeFETs), resistive RAMs (ReRAMs) and spin-orbit torque magnetic RAMs (SOT-MRAMs) in the context of IMC robustness and DNN accuracy. For a fair comparison, we carefully optimize each technology specifically for synaptic crossbar design accounting for device and circuit non-idealities. By integrating different technologies into a cross-layer simulation flow based on physical models of synaptic devices and interconnects, we present insights into various device-circuit interactions. Based on the…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Semiconductor materials and devices
