https://scholars.lib.ntu.edu.tw/handle/123456789/606981
標題: | Design optimization for ADMM-Based SVM Training Processor for Edge Computing | 作者: | Huang S.-A Hsieh Y.-Y CHIA-HSIANG YANG |
關鍵字: | alternative direction method of multipliers (ADMM);hardware-efficient realization;on-line training;rank approximation;Support vector machine (SVM);Approximation theory;Edge computing;Energy efficiency;Integrated circuit design;Matrix algebra;Optimization;Design optimization;Hardware optimization;Low rank approximations;Method of multipliers;Optimization techniques;Real-time training;Sequential minimal optimization algorithms;Training algorithms;Support vector machines | 公開日期: | 2021 | 來源出版物: | 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 | 摘要: | This paper presents an optimized support vector machine (SVM) training processor employing the alternative direction method of multipliers (ADMM) optimizer. Low-rank approximation is exploited to reduce the dimension of the kernel matrix by employing the Nystr?m method. Verified in four datasets, the proposed ADMM-based training processor with rank approximation reduces 32 of matrix dimension with only 2% drop in inference accuracy. Compared to the conventional sequential minimal optimization (SMO) algorithm, the ADMM-based training algorithm is able to achieve a 9.8 10^{7} shorter latency for training 2048 samples. Hardware optimization techniques, including pre-computation and memory sharing, are proposed to reduce the computational complexity by 62% and the memory usage by 60%. As a proof of concept, an epileptic seizure detector is designed to demonstrate the effectiveness of the proposed optimization techniques. The chip achieves a 153,310 higher energy efficiency and a 364 higher throughput-to-area ratio for SVM training than a high-end CPU. This work provides a promising solution for edge devices which require low-power and real-time training. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113345667&doi=10.1109%2fAICAS51828.2021.9458569&partnerID=40&md5=d5f3a924da880ae2243dc3942d031be4 https://scholars.lib.ntu.edu.tw/handle/123456789/606981 |
DOI: | 10.1109/AICAS51828.2021.9458569 |
顯示於: | 電機工程學系 |
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