Tiny ci-SAR A/D Converter for Deep Neural Networks in Analog in-Memory Computation

Abstract

This paper presents a tiny charge injection-Successive Approximation (ci-SAR) A/D converter (ADC) to be integrated at the periphery of analog Matrix Vector Multiplication (MVM) accelerators for Deep Neural Network (DNN) inference. Derived from the ci-SAR ADC, this converter exploits a single charge injecting cell to minimize area and energy consumption. The ADC exhibits a signal-to-noise and distortion ratio of 30.5 dB, at 5 bits of nominal resolution. The energy per conversion is 86 fJ, running at 34 MS/s, with a silicon area of $75 \mu m ^{2}$, in 22 nm technology node. From the results of our analytical framework, an SRAM-based Analog in-Memory Compute (AiMC) array, including the proposed ADC at 5 bits of resolution, can achieve an energy efficiency of 1650 TOPs/W.