SiTe CiM: Signed Ternary Computing-in-Memory for Ultra-Low Precision Deep Neural Networks

TL;DR

This paper introduces SiTe CiM, a compute-in-memory design for signed ternary neural networks that significantly reduces energy and latency, enabling more efficient deep learning hardware.

Contribution

It presents a novel SiTe CiM memory architecture with two variants, achieving substantial energy savings and throughput improvements for ternary DNNs.

Findings

Up to 88% lower CiM latency

Up to 78% energy savings in CiM operations

Up to 7X throughput boost in DNN accelerators

Abstract

Ternary Deep Neural Networks (DNN) have shown a large potential for highly energy-constrained systems by virtue of their low power operation (due to ultra-low precision) with only a mild degradation in accuracy. To enable an energy-efficient hardware substrate for such systems, we propose a compute-enabled memory design, referred to as SiTe-CiM, which features computing-in-memory (CiM) of dot products between signed ternary (SiTe) inputs and weights. SiTe CiM is based on cross-coupling of two bit cells to enable CiM of dot products in the signed ternary regime. We explore SiTe CiM with 8T-SRAM, 3T-embedded DRAM (3T-eDRAM) and 3T-ferroelectric metal FET (FEMFET) memories. We propose two flavors of this technique, namely SiTe CiM I/II. In SiTe CiM I, we employ two additional transistors per cell for cross-coupling, achieving fast CiM operations, albeit incurring an area overhead ranging from 18% to 34% (compared to standard ternary memories). In SiTe CiM II, four extra transistors are utilized for every 16 cells in a column, thereby incurring only 6% area cost (but leading to slower CiM than SiTe CiM I). Based on the array analysis, our designs achieve up to 88% lower CiM latency and 78% CiM energy savings across various technologies considered, as compared to their respective near-memory computing counterparts. Further, we perform system level analysis by incorporating SiTe CiM I/II arrays in a ternary DNN accelerator and show up to 7X throughput boost and up to 2.5X energy reduction compared to the near-memory ternary DNN accelerators.