A compression strategy to accelerate LSTM meta-learning on FPGA
NianYi Wang,
Jing Nie,
JingBin Li,
Kang Wang,
ShunKang Ling
Affiliations
NianYi Wang
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
Jing Nie
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China; Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China; Corresponding author at: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
JingBin Li
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China; Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
Kang Wang
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
ShunKang Ling
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
Driven by edge computing, how to efficiently deploy the meta-learner LSTM in the resource constrained FPGA terminal equipment has become a big problem. This paper proposes a compression strategy based on LSTM meta-learning model, which combined the structured pruning of the weight matrix and the mixed precision quantization. The weight matrix was pruned into a sparse matrix, then the weight was quantified to reduce resource consumption. Finally, a LSTM meta-learning accelerator was designed based on the idea of hardware–software cooperation. Experiments show that compared with mainstream hardware platforms, the proposed accelerator achieves at least 50.14 times increase in energy efficiency.
