# Towards Hardware Implementation of Neural Network-based Communication Algorithms

## TL;DR

This paper explores the practical implementation of neural network-based communication algorithms on hardware like FPGAs and ASICs, focusing on fixed-point quantization to enable real-time, efficient inference with minimal performance loss.

## Contribution

It demonstrates that neural network algorithms can be effectively quantized and implemented in fixed-point arithmetic on hardware, bridging the gap between simulation and practical deployment.

## Key findings

- Fixed-point neural network inference achieves negligible performance loss.
- Hardware implementation compatible with FPGAs and ASICs.
- Quantization reduces complexity while maintaining accuracy.

## Abstract

There is a recent interest in neural network (NN)-based communication algorithms which have shown to achieve (beyond) state-of-the-art performance for a variety of problems or lead to reduced implementation complexity. However, most work on this topic is simulation based and implementation on specialized hardware for fast inference, such as field-programmable gate arrays (FPGAs), is widely ignored. In particular for practical uses, NN weights should be quantized and inference carried out by a fixed-point instead of floating-point system, widely used in consumer class computers and graphics processing units (GPUs). Moving to such representations enables higher inference rates and complexity reductions, at the cost of precision loss. We demonstrate that it is possible to implement NN-based algorithms in fixed-point arithmetic with quantized weights at negligible performance loss and with hardware complexity compatible with practical systems, such as FPGAs and application-specific integrated circuits (ASICs).