Energy awareness in low precision neural networks

02/06/2022
by   Nurit Spingarn-Eliezer, et al.
0

Power consumption is a major obstacle in the deployment of deep neural networks (DNNs) on end devices. Existing approaches for reducing power consumption rely on quite general principles, including avoidance of multiplication operations and aggressive quantization of weights and activations. However, these methods do not take into account the precise power consumed by each module in the network, and are therefore not optimal. In this paper we develop accurate power consumption models for all arithmetic operations in the DNN, under various working conditions. We reveal several important factors that have been overlooked to date. Based on our analysis, we present PANN (power-aware neural network), a simple approach for approximating any full-precision network by a low-power fixed-precision variant. Our method can be applied to a pre-trained network, and can also be used during training to achieve improved performance. In contrast to previous methods, PANN incurs only a minor degradation in accuracy w.r.t. the full-precision version of the network, even when working at the power-budget of a 2-bit quantized variant. In addition, our scheme enables to seamlessly traverse the power-accuracy trade-off at deployment time, which is a major advantage over existing quantization methods that are constrained to specific bit widths.

READ FULL TEXT
research
05/30/2022

FBM: Fast-Bit Allocation for Mixed-Precision Quantization

Quantized neural networks are well known for reducing latency, power con...
research
02/07/2020

Switchable Precision Neural Networks

Instantaneous and on demand accuracy-efficiency trade-off has been recen...
research
05/24/2016

An Analysis of Deep Neural Network Models for Practical Applications

Since the emergence of Deep Neural Networks (DNNs) as a prominent techni...
research
05/20/2022

Deployment of Energy-Efficient Deep Learning Models on Cortex-M based Microcontrollers using Deep Compression

Large Deep Neural Networks (DNNs) are the backbone of today's artificial...
research
11/05/2018

ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

Despite numerous state-of-the-art applications of Deep Neural Networks (...
research
02/27/2022

Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization

Cardiovascular disease (CVDs) is one of the universal deadly diseases, a...
research
10/30/2019

Training DNN IoT Applications for Deployment On Analog NVM Crossbars

Deep Neural Networks (DNN) applications are increasingly being deployed ...

Please sign up or login with your details

Forgot password? Click here to reset