# IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array

arXiv: https://arxiv.org/abs/2003.12558 · HTML: https://mlsanity.com/paper/2003.12558/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications](https://mlsanity.com/paper/2203.04737/) (2022)
- [A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness](https://mlsanity.com/paper/2204.00426/) (2022)
- [WWW: What, When, Where to Compute-in-Memory](https://mlsanity.com/paper/2312.15896/) (2023)
- [OPTIMA: Design-Space Exploration of Discharge-Based In-SRAM Computing: Quantifying Energy-Accuracy Trade-Offs](https://mlsanity.com/paper/2411.06846/) (2024)

## Abstract (arXiv)

`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck. This emerging paradigm aims at embedding some aspects of computations inside the memory array, thereby avoiding frequent and expensive movement of data between the compute unit and the storage memory. In-memory computing with respect to Silicon memories has been widely explored on various memory bit-cells. Embedding computation inside the 6 transistor (6T) SRAM array is of special interest since it is the most widely used on-chip memory. In this paper, we present a novel in-memory multiplication followed by accumulation operation capable of performing parallel dot products within 6T SRAM without any changes to the standard bitcell. We, further, study the effect of circuit non-idealities and process variations on the accuracy of the LeNet-5 and VGG neural network architectures against the MNIST and CIFAR-10 datasets, respectively. The proposed in-memory dot-product mechanism achieves 88.8% and 99% accuracy for the CIFAR-10 and MNIST, respectively. Compared to the standard von Neumann system, the proposed system is 6.24x better in energy consumption and 9.42x better in delay.