# Correlation Manipulating Circuits for Stochastic Computing

arXiv: https://arxiv.org/abs/1803.04862 · HTML: https://mlsanity.com/paper/1803.04862/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI](https://mlsanity.com/paper/2412.17966/) (2024)
- [Implementation Of Tiny Machine Learning Models On Arduino 33 BLE For Gesture And Speech Recognition](https://mlsanity.com/paper/2207.12866/) (2022)
- [BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML](https://mlsanity.com/paper/2111.06686/) (2021)

## Abstract (arXiv)

Stochastic computing (SC) is an emerging computing technique that promises high density, low power, and error tolerant solutions. In SC, values are encoded as unary bitstreams and SC arithmetic circuits operate on one or more bitstreams. In many cases, the input bitstreams must be correlated or uncorrelated for SC arithmetic to produce accurate results. As a result, a key challenge for designing SC accelerators is manipulating the impact of correlation across SC operations. This paper presents and evaluates a set of novel correlation manipulating circuits to manage correlation in SC computation: a synchronizer, desynchronizer, and decorrelator. We then use these circuits to propose improved SC maximum, minimum, and saturating adder designs. Compared to existing correlation manipulation techniques, our circuits are more accurate and up to 3x more energy efficient. In the context of an image processing pipeline, these circuits can reduce the total energy consumption by up to 24%.