# OPTIMA: Design-Space Exploration of Discharge-Based In-SRAM Computing: Quantifying Energy-Accuracy Trade-Offs

arXiv: https://arxiv.org/abs/2411.06846 · HTML: https://mlsanity.com/paper/2411.06846/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks](https://mlsanity.com/paper/1805.03718/) (2018)
- [CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference](https://mlsanity.com/paper/2107.02388/) (2021)
- [IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array](https://mlsanity.com/paper/2003.12558/) (2020)
- [AID: Accuracy Improvement of Analog Discharge-Based in-SRAM Multiplication Accelerator](https://mlsanity.com/paper/2204.07387/) (2022)
- [SMART: Investigating the Impact of Threshold Voltage Suppression in an In-SRAM Multiplication/Accumulation Accelerator for Accuracy Improvement in 65 nm CMOS Technology](https://mlsanity.com/paper/2209.04434/) (2022)

## Abstract (arXiv)

In-SRAM computing promises energy efficiency, but circuit nonlinearities and PVT variations pose major challenges in designing robust accelerators. To address this, we introduce OPTIMA, a modeling framework that aids in analyzing bit-line discharge and power consumption in 6T-SRAM-based accelerators. It provides insights into limiting factors and enables fast design-space exploration of circuit configurations. Leveraging OPTIMA for in-SRAM multiplications exhibits ~100x simulation speed-up while maintaining an RMS modeling error of 0.88mV. Exploration yields an optimized multiplier with 1.05pJ energy consumption per 4-bit operation and classification accuracies of 71.8% (top-1) and 90.4% (top-5) for ImageNet and 92.5% for CIFAR-10 datasets respectively when applied in quantized DNNs. To further support research and development, we made our tool flow available open source at https://github.com/sevjaeg/optima.