A Mobile DNN Training Processor With Automatic Bit Precision Search and Fine-Grained Sparsity Exploitation

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In this article, an energy-efficient deep learning processor is proposed for deep neural network (DNN) training in mobile platforms. Conventional mobile DNN training processors suffer from high-bit precision requirement and high ReLU-dependencies. The proposed processor breaks through these fundamental issues by adopting three new features. It first combines the runtime automatic bit precision searching method addition to both conventional dynamic fixed-point representation and stochastic rounding to realize low-precision training. It adopts bit-slice scalable core architecture with the input skipping functionality to exploit bit-slice-level fine-grained sparsity. The iterative channel reordering unit helps the processor to maintain high core utilization by solving the workload unbalancing problem during zero-slice skipping. It finally achieves at least 4.4x higher energy efficiency compared with the conventional DNN training processors.
Publisher
IEEE COMPUTER SOC
Issue Date
2022-03
Language
English
Article Type
Article
Citation

IEEE MICRO, v.42, no.2, pp.16 - 24

ISSN
0272-1732
DOI
10.1109/MM.2021.3135457
URI
http://hdl.handle.net/10203/296647
Appears in Collection
EE-Journal Papers(저널논문)
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