부산대학교 도서관

Edge training of deep neural networks (DNNs) is a desirable goal for continuous learning; however, it is hindered by the enormous computational power required by training. Hardware approximate multipliers have shown their effectiveness in gaining resource efficiency in DNN inference accelerators; however, training with approximate multipliers is largely unexplored. To build resource-efficient accelerators with approximate multipliers supporting DNN training, a thorough evaluation of training convergence and accuracy for different DNN architectures and different approximate multipliers is needed. This article presents ApproxTrain, an open-source framework that allows fast evaluation of DNN training and inference using simulated approximate multipliers. ApproxTrain is as user-friendly as TensorFlow (TF) and requires only a high-level description of a DNN architecture along with C/C++ functional models of the approximate multiplier. We improve the speed of the simulation at the multiplier level by using a novel LUT-based approximate floating-point (FP) multiplier simulator on GPU (AMSim). Additionally, a novel flow is presented to seamlessly convert C/C++ functional models of approximate FP multipliers into AMSim. ApproxTrain leverages CUDA and efficiently integrates AMSim into the TensorFlow library to overcome the absence of native hardware approximate multiplier in commercial GPUs. We use ApproxTrain to evaluate the convergence and accuracy performance of DNN training with approximate multipliers for three application domains: image classification, object detection, and neural machine translation. The evaluations demonstrate similar convergence behavior and negligible change in test accuracy compared to FP32 and Bfloat16 multipliers. Compared to CPU-based approximate multiplier simulations in training and inference, the GPU-accelerated ApproxTrain is more than $2500\times $ faster. Based on highly optimized closed-source cuDNN/cuBLAS libraries with native hardware multipliers, the original TensorFlow is, on average, only $8\times $ faster than ApproxTrain.