Computer Science > Hardware Architecture
arXiv:2502.13873 (cs)
[Submitted on 19 Feb 2025 (v1), last revised 17 Mar 2025 (this version, v2)]
Title:NVR: Vector Runahead on NPUs for Sparse Memory Access
Authors:Hui Wang,Zhengpeng Zhao,Jing Wang,Yushu Du,Yuan Cheng,Bing Guo,He Xiao,Chenhao Ma,Xiaomeng Han,Dean You,Jiapeng Guan,Ran Wei,Dawei Yang,Zhe Jiang
View a PDF of the paper titled NVR: Vector Runahead on NPUs for Sparse Memory Access, by Hui Wang and 13 other authors
View PDFHTML (experimental)Abstract:Deep Neural Networks are increasingly leveraging sparsity to reduce the scaling up of model parameter size. However, reducing wall-clock time through sparsity and pruning remains challenging due to irregular memory access patterns, leading to frequent cache misses. In this paper, we present NPU Vector Runahead (NVR), a prefetching mechanism tailored for NPUs to address cache miss problems in sparse DNN workloads. Rather than optimising memory patterns with high overhead and poor portability, NVR adapts runahead execution to the unique architecture of NPUs. NVR provides a general micro-architectural solution for sparse DNN workloads without requiring compiler or algorithmic support, operating as a decoupled, speculative, lightweight hardware sub-thread alongside the NPU, with minimal hardware overhead (under 5%). NVR achieves an average 90% reduction in cache misses compared to SOTA prefetching in general-purpose processors, delivering 4x average speedup on sparse workloads versus NPUs without prefetching. Moreover, we investigate the advantages of incorporating a small cache (16KB) into the NPU combined with NVR. Our evaluation shows that expanding this modest cache delivers 5x higher performance benefits than increasing the L2 cache size by the same amount.
Subjects: | Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI) |
Cite as: | arXiv:2502.13873 [cs.AR] |
(orarXiv:2502.13873v2 [cs.AR] for this version) | |
https://doi.org/10.48550/arXiv.2502.13873 arXiv-issued DOI via DataCite |
Submission history
From: Hui Wang [view email][v1] Wed, 19 Feb 2025 16:54:58 UTC (1,845 KB)
[v2] Mon, 17 Mar 2025 20:31:46 UTC (1,845 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled NVR: Vector Runahead on NPUs for Sparse Memory Access, by Hui Wang and 13 other authors
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.