Amphi: Practical and Intelligent Data Prefetching for the First-Level Cache
Xuan Tang, Zicong Wang, Shuiyi He, Dezun Dong, Xiangke Liao · 2025
Data prefetchers play a crucial role in alleviating the memory wall by predicting future memory accesses. First-level cache prefetchers can observe all memory instructions but often rely on simpler strategies due to limited resources. While emerging machine learning-based approaches cover more memory access patterns, they typically require higher computational and storage resources and are usually deployed in the last-level cache. Other intelligent solutions for the first-level cache show only modest performance gains. To address this, we propose Amphi, the first practical and intelligent data prefetcher specifically designed for the first-level cache. Applying a binarized temporal convolutional network, Amphi significantly reduces storage overhead while maintaining performance comparable to the SOTA intelligent prefetcher. With a storage overhead of only 3.4 KB, Amphi requires only one-eighth of Pythia's storage needs. Amphi paves the way for the broader adoption of intelligence-driven prefetching solutions.