A compression strategy to accelerate LSTM meta-learning on FPGA

A compression strategy to accelerate LSTM meta-learning on FPGA

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 mix...

Full description

Bibliographic Details
Main Authors: NianYi Wang, Jing Nie, JingBin Li, Kang Wang, ShunKang Ling
Format: Article
Language:English
Published: Elsevier 2022-09-01
Series:ICT Express
Subjects:
Edge calculation
FPGA
LSTM Meta-Learning Accelerator
Structural pruning
Mixed precision quantization
Online Access:http://www.sciencedirect.com/science/article/pii/S2405959522000558

Internet

http://www.sciencedirect.com/science/article/pii/S2405959522000558

Similar Items