Time-Based Compute-in-Memory for Cryogenic Neural Network With Successive Approximation Register Time-to-Digital Converter

Abstract

This article explores a compute-in-memory (CIM) paradigm’s new application for cryogenic neural network. Using the 28-nm cryogenic transistor model calibrated at 4 K, the time-based CIM macro comprised of the following: 1) area-efficient unit delay cell design for cryogenic operation and 2) area and power efficient, and a high-resolution achievable successive approximation register (SAR) time-to-digital converter (TDC) is proposed. The benchmark simulation first shows that the proposed macro has better latency than the current-based CIM counterpart. Next, the simulation further shows that it has better scalability for a larger size decoder design and process technology optimization.