Boosting LSTM Performance Through Dynamic Precision Selection

Abstract

The use of low numerical precision is a fundamental optimization included in modern accelerators for Deep Neural Networks (DNNs). The number of bits of the numerical representation is set to the minimum precision that is able to retain accuracy based on an offline profiling, and it is kept constant for DNN inference. In this work, we explore the use of dynamic precision selection during DNN inference. We focus on Long Short Term Memory (LSTM) networks, which represent the state-of-the-art networks for applications such as machine translation and speech recognition. Unlike conventional DNNs, LSTM networks remember information from previous evaluations by storing data in the LSTM cell state. Our key observation is that the cell state determines the amount of precision required: time steps where the cell state changes significantly require higher precision, whereas time steps where the cell state is stable can be computed with lower precision without any loss in accuracy. Based on this observation, we implement a novel hardware scheme that tracks the evolution of the elements in the LSTM cell state and dynamically selects the appropriate precision in each time step. For a set of popular LSTM networks, our scheme selects the lowest precision for more than 66% of the time, outperforming systems that fix the precision statically. We evaluate our proposal on top of a modern accelerator highly optimized for LSTM computation, and show that it provides 1.56x speedup and 23% energy savings on average without any loss in accuracy. The extra hardware to determine the appropriate precision represents a small area overhead of 8.8%.