# Bias-Scalable Near-Memory CMOS Analog Processor for Machine Learning

arXiv: https://arxiv.org/abs/2202.05022 · HTML: https://mlsanity.com/paper/2202.05022/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [Multiplierless MP-Kernel Machine For Energy-efficient Edge Devices](https://mlsanity.com/paper/2106.01958/) (2021)

## Built on by (most cited first, 2 of 2)

- [Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning](https://mlsanity.com/paper/2205.05664/) (2022)
- [KALAM: toolKit for Automating high-Level synthesis of Analog computing systeMs](https://mlsanity.com/paper/2410.22946/) (2024)

## Abstract (arXiv)

Bias-scalable analog computing is attractive for implementing machine learning (ML) processors with distinct power-performance specifications. For instance, ML implementations for server workloads are focused on higher computational throughput for faster training, whereas ML implementations for edge devices are focused on energy-efficient inference. In this paper, we demonstrate the implementation of bias-scalable approximate analog computing circuits using the generalization of the margin-propagation principle called shape-based analog computing (S-AC). The resulting S-AC core integrates several near-memory compute elements, which include: (a) non-linear activation functions; (b) inner-product compute circuits; and (c) a mixed-signal compressive memory, all of which can be scaled for performance or power while preserving its functionality. Using measured results from prototypes fabricated in a 180nm CMOS process, we demonstrate that the performance of computing modules remains robust to transistor biasing and variations in temperature. In this paper, we also demonstrate the effect of bias-scalability and computational accuracy on a simple ML regression task.