Low Complexity Gradient Computation Techniques to Accelerate Deep Neural Network Training.

Abstract

Deep neural network (DNN) training is an iterative process of updating network weights, called gradient computation, where (mini-batch) stochastic gradient descent (SGD) algorithm is generally used. Since SGD inherently allows gradient computations with noise, the proper approximation of computing weight gradients within SGD noise can be a promising technique to save energy/time consumptions during DNN training. This article proposes two novel techniques to reduce the computational complexity of the gradient computations for the acceleration of SGD-based DNN training. First, considering that the output predictions of a network (confidence) change with training inputs, the relation between the confidence and the magnitude of the weight gradient can be exploited to skip the gradient computations without seriously sacrificing the accuracy, especially for high confidence inputs. Second, the angle diversity-based approximations of intermediate activations for weight gradient calculation are also presented. Based on the fact that the angle diversity of gradients is small (highly uncorrelated) in the early training epoch, the bit precision of activations can be reduced to 2-/4-/8-bit depending on the resulting angle error between the original gradient and quantized gradient. The simulations show that the proposed approach can skip up to 75.83% of gradient computations with negligible accuracy degradation for CIFAR-10 dataset using ResNet-20. Hardware implementation results using 65-nm CMOS technology also show that the proposed training accelerator achieves up to 1.69x energy efficiency compared with other training accelerators.