# Applying the Residue Number System to Network Inference

arXiv: https://arxiv.org/abs/1712.04614 · HTML: https://mlsanity.com/paper/1712.04614/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2017.

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

- [Number Systems for Deep Neural Network Architectures: A Survey](https://mlsanity.com/paper/2307.05035/) (2023)

## Abstract (arXiv)

This work explores the lesser studied objective of optimizing the multiply-and-accumulates executed during evaluation of the network. In particular, we propose using the Residue Number System (RNS) as the internal number representation across all layer evaluations, allowing us to explore usage of the more power-efficient RNS multipliers and adders. Using results from simulation of our RNS arithmetic block implementations, we show theoretical power advantages of using RNS for an end-to-end evaluator.