MERE: Hardware-Software Co-Design for Masking Cache Miss Latency in Embedded Processors

Runahead execution is a technique to mask memory latency caused by irregular memory accesses. By pre-executing the application code during occurrences of long-latency operations and prefetching anticipated cache-missed data into the cache hierarchy, runahead effectively masks memory latency for subsequent cache misses and achieves high prefetching accuracy; however, this technique has been limited to superscalar out-of-order and superscalar in-order cores. For implementation in scalar in-order cores, the challenges of area-/energy-constraint and severe cache contention remain. Here, we build the first full-stack system featuring runahead, MERE , from SoC and a dedicated ISA to the OS and programming model. Through this deployment, we show that enabling runahead in scalar in-order cores is possible, with minimal area and power overheads, while still achieving high performance. By re-constructing the sequential runahead employing a hardware/software co-design approach, the system can be implemented on a mature processor and SoC. Building on this, an adaptive runahead mechanism is proposed to mitigate the severe cache contention in scalar in-order cores. Combining this, we provide a comprehensive solution for embedded processors managing irregular workloads. Our evaluation demonstrates that the proposed MERE attains 93.5% of a 2-wide out-of-order core’s performance while constraining area and power overheads below 5%, with the adaptive runahead mechanism delivering an additional 20.1% performance gain through mitigating the severe cache contention issues.

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Publication Details

Journal
ACM Transactions on Embedded Computing Systems
Published
2025-08-27
DOI
https://doi.org/10.1145/3762654
Citations
1
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
2.27

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article

MERE: Hardware-Software Co-Design for Masking Cache Miss Latency in Embedded Processors

Jinyue Jiang, Wenbo Xu, Zhihang Tan, Shuai Zhao et al.
1 citations
ACM Transactions on Embedded Computing Systems
Parallel Computing and Optimization Techniques
2.27
article

MERE: Hardware-Software Co-Design for Masking Cache Miss Latency in Embedded Processors

Jinyue Jiang, Wenbo Xu, Zhihang Tan, Shuai Zhao, X.J. Wang, Ran Wei, Zhe Jiang, Yu Du, Jiapeng Guan, Wang Hui, Da You
article en
1 citations

Abstract

Runahead execution is a technique to mask memory latency caused by irregular memory accesses. By pre-executing the application code during occurrences of long-latency operations and prefetching anticipated cache-missed data into the cache hierarchy, runahead effectively masks memory latency for subsequent cache misses and achieves high prefetching accuracy; however, this technique has been limited to superscalar out-of-order and superscalar in-order cores. For implementation in scalar in-order cores, the challenges of area-/energy-constraint and severe cache contention remain. Here, we build the first full-stack system featuring runahead, MERE , from SoC and a dedicated ISA to the OS and programming model. Through this deployment, we show that enabling runahead in scalar in-order cores is possible, with minimal area and power overheads, while still achieving high performance. By re-constructing the sequential runahead employing a hardware/software co-design approach, the system can be implemented on a mature processor and SoC. Building on this, an adaptive runahead mechanism is proposed to mitigate the severe cache contention in scalar in-order cores. Combining this, we provide a comprehensive solution for embedded processors managing irregular workloads. Our evaluation demonstrates that the proposed MERE attains 93.5% of a 2-wide out-of-order core’s performance while constraining area and power overheads below 5%, with the adaptive runahead mechanism delivering an additional 20.1% performance gain through mitigating the severe cache contention issues.

ACM Transactions on Embedded Computing SystemsVol. 24(5s)
Dalian University of Technology (CN), Southeast University (BD), Huazhong University of Science and Technology (CN), University of York (GB), Lancaster University (GB)
National Natural Science Foundation of China, Southeast University, National Key Research and Development Program of China
Openalex Percentile: Top 12%
Parallel Computing and Optimization Techniques
2.27
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