# Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning

arXiv: https://arxiv.org/abs/2601.17615 · HTML: https://mlsanity.com/paper/2601.17615/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 198 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 21 of 21)

- [Device Placement Optimization with Reinforcement Learning](https://mlsanity.com/paper/1706.04972/) (2017)
- [ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning](https://mlsanity.com/paper/2009.02010/) (2020)
- [Learning Memory Access Patterns](https://mlsanity.com/paper/1803.02329/) (2018)
- [An Imitation Learning Approach for Cache Replacement](https://mlsanity.com/paper/2006.16239/) (2020)
- [Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning](https://mlsanity.com/paper/2109.12021/) (2021)
- [Apollo: Transferable Architecture Exploration](https://mlsanity.com/paper/2102.01723/) (2021)
- [The Championship Simulator: Architectural Simulation for Education and Competition](https://mlsanity.com/paper/2210.14324/) (2022)
- [Learning Execution through Neural Code Fusion](https://mlsanity.com/paper/1906.07181/) (2019)
- [Accelerating Deep Learning Inference via Learned Caches](https://mlsanity.com/paper/2101.07344/) (2021)
- [Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning](https://mlsanity.com/paper/2205.07394/) (2022)
- [Improving Branch Prediction By Modeling Global History with Convolutional Neural Networks](https://mlsanity.com/paper/1906.09889/) (2019)
- [DSPatch: Dual Spatial Pattern Prefetcher](https://mlsanity.com/paper/1910.03075/) (2019)
- [Reducing Load Latency with Cache Level Prediction](https://mlsanity.com/paper/2103.14808/) (2021)
- [Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies](https://mlsanity.com/paper/2106.13429/) (2021)
- [Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction](https://mlsanity.com/paper/2209.00188/) (2022)
- [Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching](https://mlsanity.com/paper/2412.05211/) (2024)
- [Puppeteer: A Random Forest-based Manager for Hardware Prefetchers across the Memory Hierarchy](https://mlsanity.com/paper/2201.12027/) (2022)
- [Triangel: A High-Performance, Accurate, Timely On-Chip Temporal Prefetcher](https://mlsanity.com/paper/2406.10627/) (2024)
- [Deep Learning based Data Prefetching in CPU-GPU Unified Virtual Memory](https://mlsanity.com/paper/2203.12672/) (2022)
- [Rethinking the Producer-Consumer Relationship in Modern DRAM-Based Systems](https://mlsanity.com/paper/2401.16279/) (2024)
- [Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.20507/) (2025)

## Built on by (most cited first, 1 of 1)

- [Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage](https://mlsanity.com/paper/2603.14583/) (2026)

## Abstract (arXiv)

Prefetching and off-chip prediction are two techniques proposed to hide long memory access latencies in high-performance processors. In this work, we demonstrate that: (1) prefetching and off-chip prediction often provide complementary performance benefits, yet (2) naively combining them often fails to realize their full performance potential, and (3) existing prefetcher control policies leave significant room for performance improvement behind. Our goal is to design a holistic framework that can autonomously learn to coordinate an off-chip predictor with multiple prefetchers employed at various cache levels. To this end, we propose a new technique called Athena, which models the coordination between prefetchers and off-chip predictor (OCP) as a reinforcement learning (RL) problem. Athena acts as the RL agent that observes multiple system-level features (e.g., prefetcher/OCP accuracy, bandwidth usage) over an epoch of program execution, and uses them as state information to select a coordination action (i.e., enabling the prefetcher and/or OCP, and adjusting prefetcher aggressiveness). At the end of every epoch, Athena receives a numerical reward that measures the change in multiple system-level metrics (e.g., number of cycles taken to execute an epoch). Athena uses this reward to autonomously and continuously learn a policy to coordinate prefetchers with OCP. Our extensive evaluation using a diverse set of memory-intensive workloads shows that Athena consistently outperforms prior state-of-the-art coordination policies across a wide range of system configurations with various combinations of underlying prefetchers, OCPs, and main memory bandwidths, while incurring only modest storage overhead. Athena is freely available at https://github.com/CMU-SAFARI/Athena.