Adaptive-Precision Framework for SGD Using Deep Q-Learning

Abstract

Stochastic gradient descent (SGD) is a widely-used algorithm in many applications, especially in the training process of deep learning models. Low-precision implementation for SGD has been studied as a major acceleration approach. However, if not appropriately used, low-precision implementation can deteriorate its convergence because of the rounding error when gradients become small near a local optimum. In this work, to balance throughput and algorithmic accuracy, we apply the Q-learning technique to adjust the precision of SGD automatically by designing an appropriate decision function. The proposed decision function for Q-learning takes the error rate of the objective function, its gradients, and the current precision configuration as the inputs. Q-learning then chooses proper precision adaptively for hardware efficiency and algorithmic accuracy. We use reconfigurable devices such as FPGAs to evaluate the adaptive precision configurations generated by the proposed Q-learning method. We prototype the framework using LeNet-5 model with MNIST and CIFAR10 datasets and implement it on a Xilinx KCU1500 FPGA board. In the experiments, we analyze the throughput of different precision representations and the precision-selection of our framework. The results show that the proposed framework with adapative precision increases the throughput by up to 4.3× compared to the conventional 32-bit floating point setting, and it achieves both the best hardware efficiency and algorithmic accuracy.