Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks

Abstract

Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and speech recognition, that potentially enable numerous applications. Consequently, there is a significant need to deploy trained CNNs to resource-constrained embedded systems. Inference using pretrained modern deep CNNs, however, requires significant system resources, including computation, energy, and memory space. To enable efficient implementation of trained CNNs, a viable approach is to approximate the network with an implementation-friendly model with only negligible degradation in classification accuracy. We present Ristretto, a CNN approximation framework that enables empirical investigation of the tradeoff between various number representation and word width choices and the classification accuracy of the model. Specifically, Ristretto analyzes a given CNN with respect to numerical range required to represent weights, activations, and intermediate results of convolutional and fully connected layers, and subsequently, it simulates the impact of reduced word width or lower precision arithmetic operators on the model accuracy. Moreover, Ristretto can fine-tune a quantized network to further improve its classification accuracy under a given number representation and word width configuration. Given a maximum classification accuracy degradation tolerance of 1%, we use Ristretto to demonstrate that three ImageNet networks can…