A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
<|reference_start|>A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering: To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perceptron predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perceptron hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.<|reference_end|>
@article{jamet2024a,
    title={A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive
  Prefetch Filtering},
    author={Alexandre Valentin Jamet, Georgios Vavouliotis, Daniel A. Jim'enez,
  Lluc Alvarez, Marc Casas},
    journal={arXiv preprint arXiv:2403.15181},
    year={2024},
    doi={10.1109/HPCA57654.2024.00046},
    archivePrefix={arXiv},
    eprint={2403.15181},
    primaryClass={cs.AR}
}