Towards Efficient Forward Propagation on Resource-Constrained Systems

Abstract

In this work we present key elements of DeepChip, a framework that bridges recent trends in machine learning with applicable forward propagation on resource-constrained devices. Main objective of this work is to reduce compute and memory requirements by removing redundancy from neural networks. DeepChip features a flexible quantizer to reduce the bit width of activations to 8-bit fixed-point and weights to an asymmetric ternary representation. In combination with novel algorithms and data compression we leverage reduced precision and sparsity for efficient forward propagation on a wide range of processor architectures. We validate our approach on a set of different convolutional neural networks and datasets: ConvNet on SVHN, ResNet-44 on CIFAR10 and AlexNet on ImageNet. Compared to single-precision floating point, memory requirements can be compressed by a factor of 43, 22 and 10 and computations accelerated by a factor of 5.2, 2.8 and 2.0 on a mobile processor without a loss in classification accuracy. DeepChip allows trading accuracy for efficiency, and for instance tolerating about 2% loss in classification accuracy further reduces memory requirements by a factor of 88, 29 and 13, and speeds up computations by a factor of 6.0, 4.3 and 5.0. Code related to this paper is available at: https://github.com/UniHD-CEG/ECML2018 .