CRC: Compressed Reservoir Computing on FPGA via Joint HSIC LASSO-based Pruning and Quantization

While reservoir computing (RC) networks offer advantages over traditional recurrent neural net- works in terms of training time and operational cost for time-series applications, deploying them on edge devices still presents significant challenges due to re- source constraints. Network compression, i.e., pruning and quantization, are thus of utmost importance. We propose a Compressed Reservoir Computing (CRC) framework that integrates advanced pruning and quantization techniques to optimize throughput, latency, energy efficiency, and resource utilization for FPGA- based RC accelerators.We describe the framework with a focus on HSIC LASSO as a novel pruning method that can capture non-linear dependencies between neurons. We validate our framework with time series classification and regression tasks, for which we generate FPGA accelerators. The accelerators achieve a very high throughput of up to 188 Megasamples/s with a latency of 5.32 ns, while reducing resource utilization by 12× and lowering the energy by 10× compared to a baseline hardware implementation, without compromising accuracy.