RR-SC: Run-Time Reconfigurable Framework for Stochastic Computing-Based Neural Networks on Edge Devices

Song Yuhong; Edwin Hsing-Mean Sha; Zhuge Qingfeng; Xu Rui; Wang Han

doi:10.7544/issn1000-1239.202220738

Journal of Computer Research and Development > 2024 > 61(4): 840-855. > DOI: 10.7544/issn1000-1239.202220738

Song Yuhong, Edwin Hsing-Mean Sha, Zhuge Qingfeng, Xu Rui, Wang Han. RR-SC: Run-Time Reconfigurable Framework for Stochastic Computing-Based Neural Networks on Edge Devices[J]. Journal of Computer Research and Development, 2024, 61(4): 840-855. DOI: 10.7544/issn1000-1239.202220738

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RR-SC: Run-Time Reconfigurable Framework for Stochastic Computing-Based Neural Networks on Edge Devices

School of Computer Science and Technology, East China Normal University, Shanghai 200062

Funds: This work was supported by the National Natural Science Foundation of China (61972154) and Shanghai Science and Technology Commission Project (20511101600).

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Author Bio:
Song Yuhong: born in 1997. PhD candidate. Student member of CCF. Her main research interests include embedded system, software-hardware co-design, automated machine learning, stochastic computing, and quantum computing

Edwin Hsing-Mean Sha: born in 1964. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include big data systems, high-performance intelligent computing, advanced storage, parallel/distributed systems and computing, embedded systems, scheduling and optimization, resource allocation, and quantum computing

Zhuge Qingfeng: born in 1970. PhD, professor, PhD supervisor. Member of CCF. Her main research interests include parallel architectures, embedded systems, supply-chain management, real-time systems, optimization algorithms, compilers, and scheduling

Xu Rui: born in 1996. PhD candidate. Student member of CCF. Her main research interests include non-volatile memory, optimization algorithms, and computer architecture

Wang Han: born in 1997. PhD candidate. Her main research interests include storage systems, embedded systems, optimization algorithms, machine learning, and adaptive learning systems
Received Date: August 21, 2022
Revised Date: May 18, 2023
Available Online: November 13, 2023

Graphical Abstract

Abstract

Abstract

With the development of AI democratization, deep neural networks (DNNs) have been widely applied to edge devices, such as smart phones and automated driving, etc. Stochastic computing (SC) as a promising technique performs fundamental machine learning (ML) tasks using simple logic gates instead of complicated binary arithmetic circuits. SC has advantages of low-power and low-cost DNNs execution on edge devices with constrained resources (e.g., energy, computation and memory units, etc.). However, previous SC work only designs one group of setting for fixed hardware implementation, ignoring the dynamic hardware resources (e.g., battery), which leads to low hardware efficiency and short battery life. In order to save energy for battery-powered edge devices, dynamic voltage and frequency scaling (DVFS) technique is widely used for hardware reconfiguration to prolong battery life. In this paper, we creatively propose a run-time reconfigurable framework, namely RR-SC, for SC-based DNNs and first attempt to combine hardware and software reconfigurations to satisfy the time constraint of inference and maximally save energy. RR-SC using reinforcement learning (RL) can generate multiple groups of model settings at one time, which can satisfy the accuracy constraints under different hardware settings (i.e., different voltage/frequency levels). The solution has the best accuracy and hardware efficiency trade-off. Meanwhile, the model settings are switched on a backbone model at run-time, which enables lightweight software reconfiguration. Experimental results show that RR-SC can switch the lightweight settings within 110 ms to guarantee the required real-time constraint at different hardware levels. Meanwhile, it can achieve up to 7.6 times improvement for the number of model inference with only 1% accuracy loss.

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References (30)

References

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