E-LSTM: Efficient inference of sparse LSTM on embedded heterogeneous system

Abstract

Various models with Long Short-Term Memory (LSTM) network have demonstrated prior art performances in sequential information processing. Previous LSTM-specific architectures set large on-chip memory for weight storage to alleviate the memory-bound issue and facilitate the LSTM inference in cloud computing. In this paper, E-LSTM is proposed for embedded scenarios with the consideration of the chip-area and limited data-access bandwidth. The heterogeneous hardware in E-LSTM tightly couples an LSTM co-processor with an embedded RISC-V CPU. The eSELL format is developed to represent the sparse weight matrix. With the proposed cell fusion optimization based on the inherent sparsity in computation, E-LSTM achieves up to 2.2× speedup of processing throughput.

Publication
Proceedings of the 56th Annual Design Automation Conference 2019
Runbin Shi
Runbin Shi
PhD Candidate
Junjie Liu
Junjie Liu
M.Phil Candidate
Hayden Kwok-Hay So
Hayden Kwok-Hay So
Associate Professor