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Smart Sensors and Systems pp 109–133Cite as

Energy Efficient RRAM Crossbar-Based Approximate Computing for Smart Cameras

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Abstract

Smart cameras have been applied successfully in many fields. The limited battery capacity and power efficiency restrict the local processing capacity of smart cameras. In order to shift vision processing closer to the sensors, we propose a power efficient framework for analog approximate computing with the emerging metal-oxide resistive switching random-access memory (RRAM) devices. A programmable RRAM-based approximate computing unit (RRAM-ACU) is introduced first to accelerate approximated computation, and a scalable approximate computing framework is then proposed on top of the RRAM-ACU. In order to program the RRAM-ACU efficiently, we also present a detailed configuration flow, which includes a customized approximator training scheme, an approximator-parameter-to-RRAM-state mapping algorithm, and an RRAM state tuning scheme. Simulation results on a set of diverse benchmarks demonstrate that, compared with an x86-64 CPU at 2 GHz, the RRAM-ACU is able to achieve 4.06–196.41× speedup and power efficiency of 24.59–567.98 GFLOPS/W with quality loss of 8.72 % on average. The implementation of HMAX application further demonstrates that the proposed RRAM-based approximate computing framework can achieve > 12. 8× power efficiency than the digital implementation counterparts (CPU, GPU, and FPGA).

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Notes

  1. 1.

    A neural network will tend to overfit when many weights of the network are large [28]. Overfitting is a problem that the model learns too much, including the noise, from the training data. The trained model will have poor predictive performance on the unknown testing data which are not covered by the training set.

  2. 2.

    We use ℓ 2 regularization in the training scheme. Regularization is a technique widely used in the neural network training to limit the amplitude of network weight, avoid overfitting, and improve model generalization [28]. To be specific, for the ℓ 2 regularization, a penalty of the square of the 2-norm of network weights will be proportionally added to the loss function of the network. So the error of the network and the amplitude of weights will be balanced and optimized simultaneously in the training process [28].

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Acknowledgements

This work was supported by 973 Project 2013CB329000, National Natural Science Foundation of China (No. 61373026), Brain Inspired Computing Research, Tsinghua University (20141080934), Tsinghua University Initiative Scientific Research Program, the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions.

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Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Yu Wang, Boxun Li, Lixue Xia, Tianqi Tang & Huazhong Yang

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  1. Yu Wang
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Correspondence to Yu Wang .

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Editors and Affiliations

  1. Center for Integrated Smart Sensors, Yuseong-gu, Daejeon, Korea (Republic of)

    Chong-Min Kyung

  2. System LSI Research Center, Kyushu University, Fukuoka, Japan

    Hiroto Yasuura

  3. Circuits and Systems Division, Tsinghua University, Beijing, China

    Yongpan Liu

  4. National Tsing Hua University , Hsichu, Taiwan

    Youn-Long Lin

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Wang, Y., Li, B., Xia, L., Tang, T., Yang, H. (2017). Energy Efficient RRAM Crossbar-Based Approximate Computing for Smart Cameras. In: Kyung, CM., Yasuura, H., Liu, Y., Lin, YL. (eds) Smart Sensors and Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-33201-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-33201-7_5

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