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Conference Paper
임베디드 시스템에서의 기계학습을 위한 양자화 학습 방정식에 관한 연구
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- Issue Date
- 2019-11
- Citation
- 대한전자공학회 학술 대회 (추계) 2019, pp.675-678
- Publisher
- 대한전자공학회
- Language
- Korean
- Type
- Conference Paper
- Abstract
- We propose that a quantized learning equation for machine learning on an embedded system, in this paper. The proposed learning scheme can minimize quantization errors and is suitable for embedded systems when quantization is composed of integer or fixed-point fraction values applied to an optimization algorithm. Moreover, by the proposed methodology, it is possible to implement a machine learning algorithm capable of exhibiting sufficient optimization performance even in low-performance hardware. The simulation results show that the optimization solver based on the proposed quantized method provides sufficient performance.
- KSP Keywords
- Machine Learning Algorithms, Optimization algorithm, Optimization performance, Quantization Error, embedded system, fixed point, simulation results