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E2BNet: MAC-free yet accurate 2-level binarized neural network accelerator for embedded systems

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Abstract

Deep neural networks are widely used in computer vision, pattern recognition, and speech recognition and achieve high accuracy at the cost of remarkable computation. High computational complexity and memory accesses of such networks create a big challenge for using them in resource-limited and low-power embedded systems. Several binary neural networks have been proposed that exploit only 1-bit values for both weights and activations. Binary neural networks substitute complex multiply-accumulation operations with bitwise logic operations to reduce computations and memory usage. However, these quantized neural networks suffer from accuracy loss, especially in big datasets. In this paper, we introduce a quantized neural network with 2-bit weights and activations that is more accurate compared to the state-of-the-art quantized neural networks, and also the accuracy is close to the full precision neural networks. Moreover, we propose E2BNet, an efficient MAC-free hardware architecture that increases power efficiency and throughput/W about 3.6 × and 1.5 × , respectively, compared to the state-of-the-art quantized neural networks. E2BNet processes more than 500 images/s on the ImageNet dataset that not only meet real-time requirements of images/video processing but also can be deployed on high frame rate video applications.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Seyed Ahmad Mirsalari, Najmeh Nazari, Seyed Ali Ansarmohammadi & Mostafa E. Salehi

  2. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Mostafa E. Salehi

  3. Electrical and Computer Engineering Department, University of California, Davis, USA

    Soheil Ghiasi

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  1. Seyed Ahmad Mirsalari
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  2. Najmeh Nazari
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  3. Seyed Ali Ansarmohammadi
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  4. Mostafa E. Salehi
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Correspondence to Najmeh Nazari.

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Mirsalari, S.A., Nazari, N., Ansarmohammadi, S.A. et al. E2BNet: MAC-free yet accurate 2-level binarized neural network accelerator for embedded systems. J Real-Time Image Proc 18, 1285–1299 (2021). https://doi.org/10.1007/s11554-021-01148-1

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