HASS: Hardware-aware sparsity search for dataflow DNN accelerator
File(s)
Author(s)
Type
Conference Paper
Abstract
Deep Neural Networks (DNNs) excel in learning hierarchical representations from raw data, such as images, audio, and text. To compute these DNN models with high performance and energy efficiency, these models are usually deployed onto customized hardware accelerators. Among various accelerator designs, dataflow architecture has shown promising performance due to its layer-pipelined structure and its scalability in data parallelism.Exploiting weights and activations sparsity can further enhance memory storage and computation efficiency. However, existing approaches focus on exploiting sparsity in non-dataflow accelerators, which cannot be applied onto dataflow accelerators because of the large hardware design space introduced. As such, this could miss opportunities to find an optimal combination of sparsity features and hardware designs.In this paper, we propose a novel approach to exploit unstructured weights and activations sparsity for dataflow accelerators, using software and hardware co-optimization. We propose a Hardware-Aware Sparsity Search (HASS) to systematically determine an efficient sparsity solution for dataflow accelerators. Over a set of models, we achieve an efficiency improvement ranging from 1.3 × to 4.2 × compared to existing sparse designs, which are either non-dataflow or non-hardware-aware. Particularly, the throughput of MobileNetV3 can be optimized to 4895 images per second. HASS is open-source: https://github.com/Yu-Zhewen/HASS
Date Issued
2024-01-01
Date Acceptance
2024-05-29
Citation
2024 34th International Conference on Field-Programmable Logic and Applications (FPL), 2024, pp.257-263
ISBN
979-8-3315-3007-5
Publisher
IEEE
Start Page
257
End Page
263
Journal / Book Title
2024 34th International Conference on Field-Programmable Logic and Applications (FPL)
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
International Conference on Field-Programmable Logic and Applications
Publication Status
Published
Start Date
2024-09-02
Finish Date
2024-09-06
Coverage Spatial
Turin, Italy
Date Publish Online
2024-01-01