Learning Forward Reuse Distance

Li, Pengcheng; Gu, Yongbin

doi:10.48550/arxiv.2007.15859

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…The replacer in Catcher then uses P LRU∼LFU to decide which replacement policy to choose when the cache misses, which avoids the need to predict every block in the cache (replacement policy will determine which block is replaced, Catcher does not need to specify exactly). Compared with previous studies (Song et al 2020;Liu et al 2020;Shi et al 2019;Li and Gu 2020), Catcher can reduce large-scale operations and improve operational efficiency (when there are many blocks in the cache, the computation overhead and time delay of predicting all blocks will be huge when each round of requests arrives). The actions collected by AW in Catcher come from the second half of s t and the first half of s t−1 to reflect the probability distribution of the replacement policy when the state changes from s t−1 to s t .…”

Section: Ddpg For Catchermentioning

confidence: 91%

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An End-to-End Automatic Cache Replacement Policy Using Deep Reinforcement Learning

Zhang¹,

Wang²,

Shi³

et al. 2022

ICAPS

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In the past few decades, much research has been conducted on the design of cache replacement policies. Prior work frequently relies on manually-engineered heuristics to capture the most common cache access patterns, or predict the reuse distance and try to identify the blocks that are either cache-friendly or cache-averse. Researchers are now applying recent advances in machine learning to guide cache replacement policy, augmenting or replacing traditional heuristics and data structures. However, most existing approaches depend on the certain environment which restricted their application, e.g, most of the approaches only consider the on-chip cache consisting of program counters (PCs). Moreover, those approaches with attractive hit rates are usually unable to deal with modern irregular workloads, due to the limited feature used. In contrast, we propose a pervasive cache replacement framework to automatically learn the relationship between the probability distribution of different replacement policies and workload distribution by using deep reinforcement learning. We train an end-to-end cache replacement policy only on the past requested address through two simple and stable cache replacement policies. Furthermore, the overall framework can be easily plugged into any scenario that requires cache. Our simulation results on 8 production storage traces run against 3 different cache configurations confirm that the proposed cache replacement policy is effective and outperforms several state-of-the-art approaches.

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Section: Ddpg For Catchermentioning

confidence: 91%

“…Other classes see (Park and Park 2017) in detail.). Li and Gu (2020) characterize the patterns of these workloads on a basis of time-series reuse distance trend and classify these workloads into six patterns like Triangle, Clouds, and so on. Similar work includes Chakraborttii and Litz (2020), Rodriguez et al (2021), etc.…”

Section: Workload Distributionmentioning

confidence: 99%

An End-to-End Automatic Cache Replacement Policy Using Deep Reinforcement Learning

Zhang¹,

Wang²,

Shi³

et al. 2022

ICAPS

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“…Early Exit [12,118,126,161,194,244,245,249,271,282,313] Model Selection [159,191,271,314] Result Cache [13,39,53,92,93,96,108,112,114,123,209,268,293,319] 3.3.1 Model Compression: Model compression techniques facilitate the deployment of resource-hungry AI models into resourceconstrained EDGE servers by reducing the complexity of the DNN. Model compression exploits the sparse nature of gradients' and computation involved while training the DNN model.…”

Section: Model Compressionmentioning

confidence: 99%

Enabling Deep Learning for All-in EDGE paradigm

Joshi¹,

Hasanuzzaman²,

Thapa³

et al. 2022

Preprint

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Deep Learning-based models have been widely investigated, and they have demonstrated significant performance on non-trivial tasks such as speech recognition, image processing, and natural language understanding. However, this is at the cost of substantial data requirements. Considering the widespread proliferation of edge devices (e.g., Internet of Things devices) over the last decade, Deep Learning in the edge paradigm, such as device-cloud integrated platforms, is required to leverage its superior performance. Moreover, it is suitable from the data requirements perspective in the edge paradigm because the proliferation of edge devices has resulted in an explosion in the volume of generated and collected data. However, there are difficulties due to other requirements such as high computation, high latency, and high bandwidth caused by Deep Learning applications in real-world scenarios. In this regard, this survey paper investigates Deep Learning at the edge, its architecture, enabling technologies, and model adaption techniques, where edge servers and edge devices participate in deep learning training and inference. For simplicity, we call this paradigm the All-in EDGE paradigm. Besides, this paper presents the key performance metrics for Deep Learning at the All-in EDGE paradigm to evaluate various deep learning techniques and choose a suitable design. Moreover, various open challenges arising from the deployment of Deep Learning at the All-in EDGE paradigm are identified and discussed.

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Learning Forward Reuse Distance

Cited by 2 publications

References 25 publications

An End-to-End Automatic Cache Replacement Policy Using Deep Reinforcement Learning

An End-to-End Automatic Cache Replacement Policy Using Deep Reinforcement Learning

Enabling Deep Learning for All-in EDGE paradigm

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