DOVA PRO: A Dynamic Overwriting Voltage Adjustment Technique for STT-MRAM L1 Cache Considering Dielectric Breakdown Effect

Chen, Jinbo; Lu, C.Z.; Ni, Jiacheng; Guo, Xiaochen; Girard, Patrick; Cheng, Yuanqing

doi:10.1109/tvlsi.2021.3073415

Cited by 3 publications

(1 citation statement)

References 61 publications

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“…To boost processing speed, a sketch's update and query process should be straightforward and ideally only access CPU caches when handling high-speed data streams [19]. CPU cache memory is divided into three levels: L1, L2 and L3, among which the L1 cache is the fastest, but of size restricted to between 8KB and 64KB in general [27,28], forcing sketches to be compact enough. Sketches with small sizes bring benefits in many practical scenarios, e.g., to compress gradients and accelerate the training process in distributed machine learning [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Tight-Sketch: A High-Performance Sketch for Heavy Item-Oriented Data Stream Mining with Limited Memory Size

Li,

Patras

2023

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

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Accurate and fast data stream mining is critical and fundamental to many tasks, including time series database handling, big data management and machine learning. Different heavy-based detection tasks, such as heavy hitter, heavy changer, persistent item and significant item detection, have drawn much attention from both the industry and academia. Unfortunately, due to the growing data stream speeds and limited memory (L1 cache) available for real-time processing, existing schemes face challenges in simultaneously achieving high detection accuracy, high memory efficiency, and fast update throughput, as we reveal. To tackle this conundrum, we propose a versatile and elegant sketch framework named Tight-Sketch, which supports a spectrum of heavybased detection tasks. Considering that most items are cold (nonheavy/persistent/significant) in practice, we employ different eviction treatments for different types of items to discard these potentially cold ones as soon as possible, and offer more protection to those that are hot (heavy/persistent/significant). In addition, we propose an eviction method that follows a stochastic decay strategy, enabling Tight-Sketch to only bear small one-sided errors (no overestimation). We present a theoretical analysis of the error bounds and conduct extensive experiments on diverse detection tasks to demonstrate that Tight-Sketch significantly outperforms existing methods in terms of accuracy and update speed. Lastly, we accelerate Tight-Sketch's update throughput by up to 36% with Single Instruction Multiple Data (SIMD) instructions. CCS CONCEPTS• Information systems → Data stream mining.

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