Work

MOSDA: On-Chip Memory Optimized Sparse Deep Neural Network Accelerator With Efficient Index Matching

Year: 2020

Type: article

Abstract: The irregular data access pattern caused by sparsity brings great challenges to efficient processing accelerators. Focusing on the index-matching property in DNN, this article aims to decompose sparse DNN processing into easy-to-handle processing tasks to maintain the utilization of processing elements. According to the proposed sparse processing dataflow, this article proposes an efficient general-purpose hardware accelerator called MOSDA, which can be effectively applied for operations of convolutional layers, fully-connected layers, and matrix multiplications. Compared to the state-of-art CNN accelerators, MOSDA achieves 1.1× better throughput and 2.1× better energy efficiency than Eyeriss v2 in sparse Alexnet in our case study.

Source: IEEE Open Journal of Circuits and Systems

DOI: 10.1109/ojcas.2020.3035402

Authors: Hongjie Xu, Jun Shiomi, Hidetoshi Onodera

Institution: Kyoto University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funders: Mentor Graphics, Ministry of Education, Culture, Sports, Science and Technology