Dynamically Adapting Floating-Point Precision to Accelerate Deep Neural Network Training

Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successfully applied to train deep neural networks. Despite its advantages in terms of reducing the need for key resources like memory bandwidth or register file size, it has a limited capacity for diminishing further computing costs, as it requires 32-bits to represent its output. On the other hand, full half-precision arithmetic fails to deliver state-of-the-art training accuracy. We design a binary tool SERP based on Intel Pin which allows us to characterize and analyze computer arithmetic usage in machine learning frameworks (Pytorch, Caffe, Tensorflow) and to emulate different floating point formats. Based on empirical observations about precision needs on representative deep neural networks, this paper proposes a seamless approach to dynamically adapt floating point arithmetic. Our dynamically adaptive methodology enables the use of full half-precision arithmetic for up to 96.4% of the computations when training state-of-the-art neural networks; while delivering comparable accuracy to 32-bit floating point arithmetic. Microarchitectural simulations indicate that our Dynamic approach accelerates training deep convolutional and recurrent networks with respect to FP32 by 1.39 × and 1.26 ×, respectively.