Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification

Wang, Peisong; Li, Fanrong; Li, Gang; Cheng, Jun

doi:10.1109/tnnls.2021.3120409

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…The binary augmented architecture (BAA) [ 20 ] is based on the binary augmented pruning (BAP) method that combines binary data and sparsity so that the convolutional layer can reach 98% sparsity. The weights and activation values are encoded.…”

Section: Experimental Assessment and Resultsmentioning

confidence: 99%

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Efficient Binary Weight Convolutional Network Accelerator for Speech Recognition

Guo

Deng

et al. 2023

Sensors

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Speech recognition has progressed tremendously in the area of artificial intelligence (AI). However, the performance of the real-time offline Chinese speech recognition neural network accelerator for edge AI needs to be improved. This paper proposes a configurable convolutional neural network accelerator based on a lightweight speech recognition model, which can dramatically reduce hardware resource consumption while guaranteeing an acceptable error rate. For convolutional layers, the weights are binarized to reduce the number of model parameters and improve computational and storage efficiency. A multichannel shared computation (MCSC) architecture is proposed to maximize the reuse of weight and feature map data. The binary weight-sharing processing engine (PE) is designed to avoid limiting the number of multipliers. A custom instruction set is established according to the variable length of voice input to configure parameters for adapting to different network structures. Finally, the ping-pong storage method is used when the feature map is an input. We implemented this accelerator on Xilinx ZYNQ XC7Z035 under the working frequency of 150 MHz. The processing time for 2.24 s and 8 s of speech was 69.8 ms and 189.51 ms, respectively, and the convolution performance reached 35.66 GOPS/W. Compared with other computing platforms, accelerators perform better in terms of energy efficiency, power consumption and hardware resource consumption.

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Section: Experimental Assessment and Resultsmentioning

confidence: 99%

“…In addition, some researchers [ 19 , 20 , 21 ] have also implemented binary neural network accelerators on datasets with 1000 classification tasks, such as ImageNet, but their energy efficiency needs to be improved.…”

Section: Introductionmentioning

confidence: 99%