Quantized deep neural networks for energy efficient hardware-based inference

Abstract

Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification accuracy, with custom hardware implementations great candidates for high-speed, accurate inference. While progress in achieving large scale, highly accurate DNNs has been made, significant energy and area are required due to massive memory accesses and computations. Such demands pose a challenge to any DNN implementation, yet it is more natural to handle in a custom hardware platform. To alleviate the increased demand in storage and energy, quantized DNNs constrain their weights (and activations) from floating-point numbers to only a few discrete levels. Therefore, storage is reduced, thereby leading to less memory accesses. In this paper, we provide an overview of different types of quantized DNNs, as well as the training approaches for them. Among the various quantized DNNs, our LightNN (Light Neural Network) approach can reduce both memory accesses and computation energy, by filling the gap between classic, full-precision and binarized DNNs. We provide a detailed comparison between LightNNs, conventional DNNs and Binarized Neural Networks (BNNs), with MNIST and CIFAR-10 datasets. In contrast to other quantized DNNs that trade-off significant amounts of accuracy for lower memory requirements, LightNNs can significantly reduce storage, energy and area while still maintaining a test error similar to a large DNN configuration. Thus, LightNNs provide more options for hardware designers to trade-off accuracy and energy.