Hardware Computation Graph for DNN Accelerator Design Automation without Inter-PU Templates
Authors: 
Jun Li, Wei Wang, Wu-Jun LiAuthors Info & Claims
Jun Li, Wei Wang, Wu-Jun LiAuthors Info & ClaimsArticle No.: 47, Pages 1 - 9
Abstract
Existing deep neural network (DNN) accelerator design automation (ADA) methods adopt architecture templates to predetermine parts of design choices and then explore the left design choices beyond templates. These templates can be classified into intra-PU templates and inter-PU templates according to the architecture hierarchy. Since templates limit the flexibility of ADA, designing effective ADA methods without templates has become an important research topic. Although there have appeared some works to enhance the flexibility of ADA by removing intra-PU templates, to the best of our knowledge no existing works have studied ADA methods without inter-PU templates. ADA with predetermined inter-PU templates is typically inefficient in terms of resource utilization, especially for DNNs with complex topology. In this paper, we propose a novel method, called hardware computation graph (HCG), for ADA without inter-PU templates. Experiments show that HCG method can achieve competitive latency while using only 1.4× ~ 5× fewer on-chip memory, compared with existing state-of-the-art ADA methods.
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Published In
October 2022
1467 pages
ISBN:9781450392174
DOI:10.1145/3508352
- Conference Chair:
- Tulika Mitra,
- Program Chairs:
- Evangeline Young,
- Jinjun Xiong
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Published: 22 December 2022
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- National Key R&D Program of China; NSFC
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ICCAD '22
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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design
October 30 - November 3, 2022
California, San Diego
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