A Joint Learning Approach to Hardware Caching and Prefetching
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908589128417280 |
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| author | Yuan, Samuel Saxena, Divyanshu Chen, Jiayi Sharma, Nihal Akella, Aditya |
| author_facet | Yuan, Samuel Saxena, Divyanshu Chen, Jiayi Sharma, Nihal Akella, Aditya |
| contents | Several learned policies have been proposed to replace heuristics for scheduling, caching, and other system components in modern systems. By leveraging diverse features, learning from historical trends, and predicting future behaviors, such models promise to keep pace with ever-increasing workload dynamism and continuous hardware evolution. However, policies trained in isolation may still achieve suboptimal performance when placed together. In this paper, we inspect one such instance in the domain of hardware caching -- for the policies of cache replacement and prefetching. We argue that these two policies are bidirectionally interdependent and make the case for training the two jointly. We propose a joint learning approach based on developing shared representations for the features used by the two policies. We present two approaches to develop these shared representations, one based on a joint encoder and another based on contrastive learning of the embeddings, and demonstrate promising preliminary results for both of these. Finally, we lay down an agenda for future research in this direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10862 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Joint Learning Approach to Hardware Caching and Prefetching Yuan, Samuel Saxena, Divyanshu Chen, Jiayi Sharma, Nihal Akella, Aditya Machine Learning Hardware Architecture Several learned policies have been proposed to replace heuristics for scheduling, caching, and other system components in modern systems. By leveraging diverse features, learning from historical trends, and predicting future behaviors, such models promise to keep pace with ever-increasing workload dynamism and continuous hardware evolution. However, policies trained in isolation may still achieve suboptimal performance when placed together. In this paper, we inspect one such instance in the domain of hardware caching -- for the policies of cache replacement and prefetching. We argue that these two policies are bidirectionally interdependent and make the case for training the two jointly. We propose a joint learning approach based on developing shared representations for the features used by the two policies. We present two approaches to develop these shared representations, one based on a joint encoder and another based on contrastive learning of the embeddings, and demonstrate promising preliminary results for both of these. Finally, we lay down an agenda for future research in this direction. |
| title | A Joint Learning Approach to Hardware Caching and Prefetching |
| topic | Machine Learning Hardware Architecture |
| url | https://arxiv.org/abs/2510.10862 |