Exploiting Model-Level Parallelism in Recurrent Neural Network Accelerators

Abstract

Recurrent Neural Networks (RNNs) have continued to facilitate rapid progress in a variety of academic and industrial fields, though their complexity continues to make efficient deployment difficult; when the RNN model size is not properly matched to hardware resources, performance can suffer from hardware under-utilization. In this work, we propose to explore model-level parallelism for LSTM-RNN accelerators in different levels of the model using a multicore design. The multi-core design proposed in this work operates in three computing modes: multi-programming mode in which independent models are executed; multithreading mode in which parallelism among layers of an LSTM model is explored and properly scheduled; and helper-core mode in which cores collaborate on a single LSTM layer in a lower model level comparing with multithread mode. Our design can achieve up to 1.98x speedup in "multi-programming" mode, a 1.91x speedup in "multithreading" mode and a 1.88x speedup in "helper-core" mode over the single-core design.