# Applying Data Compression Techniques on Systolic Neural Network Accelerator

## TL;DR

This paper explores the use of data compression techniques to enhance the efficiency of systolic neural network accelerators, addressing the challenge of large data volumes in approximate computing applications.

## Contribution

It introduces a novel approach combining data compression with neural network acceleration to improve power efficiency and data handling capabilities.

## Key findings

- Data compression reduces memory bandwidth usage.
- Improved power efficiency in neural network accelerators.
- Enhanced data handling for approximate computing applications.

## Abstract

New directions in computing and algorithms has lead to some new applications that have tolerance to imprecision. Although, These applications are creating large volumes of data which exceeds the capability of today's computing systems. Therefore, researchers are trying to find new techniques to alleviate this crisis. Approximate Computing is one promising technique that uses a trade off between precision and efficiency of computing. Acceleration is another solution that uses specialized logics in order to do computations in a way that is more power efficient. Another technique is Data compression which is used in memory systems in order to save capacity and bandwidth.