Bit-level sparsity deep earning accelerator embedded with the binary neural network: a survey

Yechengnuo Zhang

doi:10.1117/12.2674789

23 May 2023 Bit-level sparsity deep earning accelerator embedded with the binary neural network: a survey

Yechengnuo Zhang

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 1260437 (2023) https://doi.org/10.1117/12.2674789
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

Neural network accelerators provide deep learning algorithms with a faster and more energy-efficient way of computing. Among those hardware accelerators, the bit-level sparsity accelerator seeks the “0” bits in weights and activations to skip the ineffectual computation. Currently, bit-level sparsity accelerators have well explored the possible sparsity in various CNN models with different parameter precision. However, the large number of parameters in CNN occupies many memory storages and troubles designers to exploit the sparsity. Therefore, this paper introduces the Binary Neural Network (BNN) to shorten the computation time and energy consumption with acceptable losses of accuracy. In this paper, the research approach is based on both bit-level sparsity accelerators and BNN algorithms. Some current studies on bit-level sparsity accelerators are shown first, then BNN is introduced. The result provides a framework of bit-level sparsity deep learning accelerator embedded with the binary neural network.

Citation Download Citation

Yechengnuo Zhang "Bit-level sparsity deep earning accelerator embedded with the binary neural network: a survey", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 1260437 (23 May 2023); https://doi.org/10.1117/12.2674789

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KEYWORDS

Neural networks

Deep learning

Binary data

Computer architecture

Object detection

Design and modelling

Evolutionary algorithms

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