A 10T SRAM architecture with 40 % enhanced throughput for IMC applications benchmarked with CIFAR-10 dataset

Abstract

This research paper introduces a memory architecture that handles standard memory storage operations and enables in-memory computations, surpassing the capabilities of conventional SRAM bit-cells. The proposed architecture in this work effectively eliminates read-disturb issues and facilitates bit-wise operations like NAND, NOR, and XNOR, all without requiring intricate analog peripheral circuits. The suggested bit-cell architecture offers enhanced throughput compared to existing In-Memory Computing (IMC) bit-cell architectures, making it a more suitable design for IMC applications. Parallelism offers enhanced throughput due to the unique bit-cell architecture, which allows all the bit-wise operations to be achieved simultaneously in a single cycle. The validity of the suggested architecture has been confirmed through Monte-Carlo variation analysis, utilizing UMC 28 nm PDK transistor models to ensure its robustness. Furthermore, architecture is benchmarked using the CIFAR-10 dataset, which entails assessing its performance across various machine learning models via the NeuroSim Simulator. The proposed architecture offers a substantial increase of up to 40 % in throughput (TOPS/W) compared to the existing architectures. Utilizing accurate Monte-Carlo simulations with 1000 samples, the stability of the proposed 10T bit-cell is validated at worst-case PVT corners, up to 6σ variations.