Augur: An Online Learning-based Hardware Data Prefetcher using Reservoir Computing

arXiv:2611.10929v1 Announce Type: new Abstract: Machine-learning-based hardware prefetchers can learn complex access patterns, but practical deployment demands online adaptation, low-latency inference, and modest hardware cost. We present Augur, a hardware delta prefetcher based on next-generation reservoir computing (NGRC) that meets all three requirements. Augur formulates delta prediction as a history-based regression problem. It constructs a fixed-size reservoir from hashed delta features over a short context window and predicts the next delta using a single dot product. The model is updated online via one-step SGD and requires no offline pre-training, no workload-specific tuning, and no floating-point arithmetic.