🤖 AI Summary
This work addresses the lack of systematic investigation into the joint impact of quantization bit-width, sampling frequency, and hardware parameters on energy efficiency in photonic reservoir computing. The authors propose an approximate reservoir computing framework based on semiconductor lasers, which achieves a balance between high prediction accuracy and low energy consumption by co-optimizing amplitude quantization of node states and output weights, tunable sampling frequency, and injection current. Evaluated on chaotic time series prediction tasks, the approach maintains excellent performance while significantly reducing energy consumption per sample. This study is the first to systematically elucidate the influence mechanism of key parameters on the energy-performance trade-off and demonstrates the feasibility and advantages of approximate computing in photonic neuromorphic systems.
📝 Abstract
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.