HPCA 2026
Sat 31 January - Wed 4 February 2026 Sydney, Australia
co-located with HPCA/CGO/PPoPP/CC 2026
Tue 3 Feb 2026 12:10 - 12:30 at Collaroy - Caching and Prefetching Chair(s): David Schall

Cache compressions are proven effective in improving the performance of caches in conventional processors. They compress data into a smaller size, allowing caches to accommodate more blocks. This helps reduce cache misses and expensive memory accesses, ultimately improving performance. However, conventional cache compression is less effective for energy harvesting systems (EHSs) which experience frequent power failure, as many compressed blocks end up not being used before their loss upon power outage. This wastes hard-own energy, which would otherwise be used for making more program progress. To address this issue, this paper introduces Kagura, an adaptive cache compression extension with frequent power failure in mind. Specifically, Kagura disables cache compression when it finds out that many cached blocks are unlikely to be reused before a power outage. That way, Kagura avoids the energy waste on useless compressions/decompressions, and the resulting speedup is on par with the ideal intermittence-aware cache compressor.

Tue 3 Feb

Displayed time zone: Hobart change

11:30 - 12:50
Caching and PrefetchingMain Conference at Collaroy
Chair(s): David Schall Technical University of Munich
11:30
20m
Talk
Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning
Main Conference
Zhenrong Lang ETH Zürich, Rahul Bera ETH Zurich, Caroline Hengartner ETH Zürich, Konstantinos Kanellopoulos ETH Zurich, Rakesh Kumar NTNU, Mohammad Sadrosadati ETH Zürich, Onur Mutlu ETH Zurich
11:50
20m
Talk
Streamlined On-Chip Temporal Prefetching
Main Conference
Quang Duong The University of Texas at Austin, Calvin Lin The University of Texas at Austin
12:10
20m
Talk
Intermittence-Aware Cache Compression
Main Conference
Gan Fang Purdue University, Jianping Zeng Arizona State University, Yuchen Zhou Purdue University, Changhee Jung Purdue University, USA
12:30
20m
Talk
TENET-v2: Applying Relation-centric Notation to Model and Optimize Data Swizzle in the Cache of Modern NPU
Main Conference
Hanyu Zhang Zhejiang University, Fangxu Guo Zhejiang University, Liqiang Lu Zhejiang University, Long Wang Huawei Technologies, Yunfei Du Huawei Technologies, Zhe Wang Huawei Technologies, Jinghan Zhang Huawei Technologies, Jie Zhang Peking University, Chenli Xue Zhejiang University, Chengpeng Wu Zhejiang University, Ziyi Zhang Zhejiang University, Yun Liang Peking University, Size Zheng Tsinghua University, Jianwei Yin Zhejiang University