Fast Exploration of Weight Sharing Opportunities for CNN Compression

02/02/2021
by   Etienne Dupuis, et al.
0

The computational workload involved in Convolutional Neural Networks (CNNs) is typically out of reach for low-power embedded devices. There are a large number of approximation techniques to address this problem. These methods have hyper-parameters that need to be optimized for each CNNs using design space exploration (DSE). The goal of this work is to demonstrate that the DSE phase time can easily explode for state of the art CNN. We thus propose the use of an optimized exploration process to drastically reduce the exploration time without sacrificing the quality of the output.

READ FULL TEXT

page 1

page 2

research
12/07/2020

BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs

Deep Convolutional Neural Networks (CNNs) have become state-of-the art f...
research
08/30/2016

Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are one of the most successful deep...
research
02/23/2022

Shisha: Online scheduling of CNN pipelines on heterogeneous architectures

Chiplets have become a common methodology in modern chip design. Chiplet...
research
06/17/2016

YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration

Convolutional neural networks (CNNs) have revolutionized the world of co...
research
11/07/2016

Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization

Artificial neural networks have gone through a recent rise in popularity...
research
08/10/2020

HAPI: Hardware-Aware Progressive Inference

Convolutional neural networks (CNNs) have recently become the state-of-t...
research
05/09/2019

Automatic Design of Artificial Neural Networks for Gamma-Ray Detection

The goal of this work is to investigate the possibility of improving cur...

Please sign up or login with your details

Forgot password? Click here to reset