# Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks

## TL;DR

This paper introduces a hardware-software co-design approach for accurate, low-power deep neural networks using 8-bit fixed-point and power-of-two weights, enabling efficient inference with minimal accuracy loss.

## Contribution

It presents a novel method to convert floating-point DNNs into 8-bit fixed-point networks with power-of-two weights and a custom hardware accelerator for low-power, high-accuracy inference.

## Key findings

- Significant power and energy savings achieved.
- Minimal accuracy degradation on CIFAR-10 and ImageNet.
- Efficient hardware implementation with simplified arithmetic operations.

## Abstract

While Deep Neural Networks (DNNs) push the state-of-the-art in many machine learning applications, they often require millions of expensive floating-point operations for each input classification. This computation overhead limits the applicability of DNNs to low-power, embedded platforms and incurs high cost in data centers. This motivates recent interests in designing low-power, low-latency DNNs based on fixed-point, ternary, or even binary data precision. While recent works in this area offer promising results, they often lead to large accuracy drops when compared to the floating-point networks. We propose a novel approach to map floating-point based DNNs to 8-bit dynamic fixed-point networks with integer power-of-two weights with no change in network architecture. Our dynamic fixed-point DNNs allow different radix points between layers. During inference, power-of-two weights allow multiplications to be replaced with arithmetic shifts, while the 8-bit fixed-point representation simplifies both the buffer and adder design. In addition, we propose a hardware accelerator design to achieve low-power, low-latency inference with insignificant degradation in accuracy. Using our custom accelerator design with the CIFAR-10 and ImageNet datasets, we show that our method achieves significant power and energy savings while increasing the classification accuracy.