Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators

ArXi:2603.08737v1 Announce Type: cross This paper presents a compression framework for Reservoir Computing that enables systematic design-space exploration of trade-offs among quantization levels, pruning rates, model accuracy, and hardware efficiency. The proposed approach leverages a sensitivity-based pruning mechanism to identify and remove less critical quantized weights with minimal impact on model accuracy, thereby reducing computational overhead while preserving accuracy.