# TableNet: a multiplier-less implementation of neural networks for inferencing

## TL;DR

This paper introduces TableNet, a multiplier-less neural network implementation using look-up tables (LUTs) for efficient hardware inference, maintaining accuracy with reduced computational complexity.

## Contribution

The paper proposes a novel LUT-based approach to replace multipliers in neural networks, enabling multiplier-less inference with comparable accuracy and memory footprint.

## Key findings

- Achieves similar accuracy to full-precision networks
- Reduces hardware complexity by eliminating multipliers
- Applicable to various architectures like MLP and CNN

## Abstract

We consider the use of look-up tables (LUT) to simplify the hardware implementation of a deep learning network for inferencing after weights have been successfully trained. The use of LUT replaces the matrix multiply and add operations with a small number of LUTs and addition operations resulting in a completely multiplier-less implementation. We compare the different tradeoffs of this approach in terms of accuracy versus LUT size and the number of operations and show that similar performance can be obtained with a comparable memory footprint as a full precision deep neural network, but without the use of any multipliers. We illustrate this with several architectures such as MLP and CNN.