PIM-WEAVER: A High Energy-efficient, General-purpose Acceleration Architecture for String Operations in Big Data Processing

Li, Wenming; Ye, Xiaochun; Wang, Da; Zhang, Hao; Tang, Zhimin; Fan, Dongrui; Sun, Ninghui

doi:10.1016/j.suscom.2019.01.006

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…During the last decade, due to the increasing interest in emerging memory technologies, emerging applications, and the need to accelerate comparison-intensive tasks, approximate search CAM has shown its potential for applications that benefit from approx-imate match rather than exact match. Some approximate CAM applications are shown in Figure 4, including machine learning, deep learning, data analytics, and computational biology [6][7][8][9][10]50]. From the computational biology realm, genomics has been a subject of great interest, mainly due to the exponential growth of the ever-increasing sequenced data volume [51].…”

Section: Approximate Search Applicationsmentioning

confidence: 99%

Approximate Content-Addressable Memories: A Review

Garzón

Yavits

Teman

et al. 2023

Chips

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Content-addressable memory (CAM) has been part of the memory market for more than five decades. CAM can carry out a single clock cycle lookup based on the content rather than an address. Thanks to this attractive feature, CAM is utilized in memory systems where a high-speed content lookup technique is required. However, typical CAM applications only support exact matching, as opposed to approximate matching, where a certain Hamming distance (several mismatching characters between a query pattern and the dataset stored in CAM) needs to be tolerated. Recent interest in approximate search has led to the development of new CAM-based alternatives, accelerating the processing of large data workloads in the realm of big data, genomics, and other data-intensive applications. In this review, we provide an overview of approximate CAM and describe its current and potential applications that would benefit from approximate search computing.

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Section: Approximate Search Applicationsmentioning

confidence: 99%

Approximate Content-Addressable Memories: A Review

Garzón

Yavits

Teman

et al. 2023

Chips

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“…Big data is different from the traditional computer processing data. It is a massive, high growth rate and diversified information asset with stronger decision-making power, insight and discovery power and process optimization ability through the new processing mode [2]. Many enterprises can make relevant decisions on enterprise operation through the analysis of big data.…”

Section: Business Services Driven By Big Datamentioning

confidence: 99%

A Probe into China's Cross-Border e-commerce Operation Model Under the Big Data View Domain

Ju¹,

Wang²

2022

Atlantis Highlights in Computer Sciences

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In the e-commerce industry, the traditional e-commerce data has been eliminated and replaced by big data with 5V (volume, variety, velocity, value, veracity) characteristics. The high-speed big data can quickly judge from many aspects and a large number of valuable data, and help the e-commerce industry improve marketing efficiency, customer experience The benefits brought by big data to the cross-border e-commerce industry are self-evident. Starting from the development status of China's cross-border e-commerce in recent years, this paper deeply analyzes the business value brought by big data driven business services to cross-border e-commerce. Finally, it analyzes the integrated application mode of big data and cross-border e-commerce, and puts forward suggestions for the development of China's cross-border e-commerce industry.

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“…Content-addressable memories (CAMs) offer outstanding performance in applications where high-speed searching is critical [46, 55]. In addition to well-studied applications, such as network routers, digital signal processing, analytics, and reconfigurable computing [30, 46], CAMs can be used in a variety of emerging compareintensive big data workloads [34], machine learning applications [2, 23], and bioinformatics [27, 29, 53].…”

Section: Introductionmentioning

confidence: 99%

DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

Jahshan,

Merlin,

Garzón

et al. 2023

Preprint

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We propose a novel dynamic storage-based approximate search content addressable memory (DASH-CAM) for computational genomics applications, particularly for identification and classification of viral pathogens of epidemic significance. DASH-CAM provides 5.5× better density compared to state-of-the-art SRAM-based approximate search CAM. This allows using DASH-CAM as a portable classifier that can be applied to pathogen surveillance in low-quality field settings during pandemics, as well as to pathogen diagnostics at points of care. DASH-CAM approximate search capabilities allow a high level of flexibility when dealing with a variety of industrial sequencers with different error profiles. DASH-CAM achieves up to 30% and 20% higherF1score when classifying DNA reads with 10% error rate, compared to state-of-the-art DNA classification tools MetaCache-GPU and Kraken2 respectively. Simulated at 1GHz, DASH-CAM provides 1, 178× and 1, 040× average speedup over MetaCache-GPU and Kraken2 respectively.CCS CONCEPTS•Hardware→Bio-embedded electronics.

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PIM-WEAVER: A High Energy-efficient, General-purpose Acceleration Architecture for String Operations in Big Data Processing

Cited by 7 publications

References 28 publications

Approximate Content-Addressable Memories: A Review

Approximate Content-Addressable Memories: A Review

A Probe into China's Cross-Border e-commerce Operation Model Under the Big Data View Domain

DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

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