用半导体激光器实现低功耗近似储备池计算
Approximate reservoir computing with a semiconductor laser for reducing energy consumption
- 通过量化节点状态和输出权重，实现近似储备池计算
- 优化比特数、采样频率与注入电流，能耗显著降低
- 适合光子机器学习、低功耗时序预测场景
光子储备池计算是一种有前景的物理机器学习技术，用于预测时间序列数据。实现该技术需要对储备池的响应信号进行量化，而量化比特数和采样频率需权衡性能与能耗。然而，关于比特量化与采样频率影响的研究较少。本文提出一种基于半导体激光器的近似储备池计算方法，对储备池中的节点状态和输出权重进行幅度量化。在混沌时间序列预测任务中评估了性能与每样本能耗。通过优化量化比特数、采样频率及半导体激光器注入电流，实现了显著的能耗降低，同时保持了预测性能。
原文摘要 · Abstract (English)
Photonic reservoir computing is a promising physical machine-learning technique for predicting time-series data. The quantization of the response signal from the reservoir is required for the implementation of photonic reservoir computing, and the number of quantization bits and sampling frequency need to be optimized to achieve high performance and low energy consumption. However, few studies have been reported to investigate the effect of bit quantization and sampling frequency. In this study, we introduce a concept of approximate reservoir computing with a semiconductor laser by quantizing the amplitude of node states in the reservoir and output weights. We evaluate the performance of a chaotic time-series prediction task and energy consumption per sample. We achieve significant reduction of energy consumption by optimizing the number of quantization bits, the sampling frequency, and the injection current of the semiconductor laser, while maintaining the prediction performance.
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