# Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning

arXiv: https://arxiv.org/abs/2205.05664 · HTML: https://mlsanity.com/paper/2205.05664/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 3 of 3)

- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [Multiplierless MP-Kernel Machine For Energy-efficient Edge Devices](https://mlsanity.com/paper/2106.01958/) (2021)
- [Bias-Scalable Near-Memory CMOS Analog Processor for Machine Learning](https://mlsanity.com/paper/2202.05022/) (2022)

## Built on by (most cited first, 1 of 1)

- [KALAM: toolKit for Automating high-Level synthesis of Analog computing systeMs](https://mlsanity.com/paper/2410.22946/) (2024)

## Abstract (arXiv)

Analog computing is attractive compared to digital computing due to its potential for achieving higher computational density and higher energy efficiency. However, unlike digital circuits, conventional analog computing circuits cannot be easily mapped across different process nodes due to differences in transistor biasing regimes, temperature variations and limited dynamic range. In this work, we generalize the previously reported margin-propagation-based analog computing framework for designing novel textitshape-based analog computing (S-AC) circuits that can be easily cross-mapped across different process nodes. Similar to digital designs S-AC designs can also be scaled for precision, speed, and power. As a proof-of-concept, we show several examples of S-AC circuits implementing mathematical functions that are commonly used in machine learning (ML) architectures. Using circuit simulations we demonstrate that the circuit input/output characteristics remain robust when mapped from a planar CMOS 180nm process to a FinFET 7nm process. Also, using benchmark datasets we demonstrate that the classification accuracy of a S-AC based neural network remains robust when mapped across the two processes and to changes in temperature.