標題: Titlebook: Emerging Technology and Architecture for Big-data Analytics; Anupam Chattopadhyay,Chip Hong Chang,Hao Yu Book 2017 Springer International [打印本頁] 作者: minutia 時間: 2025-3-21 17:16
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書目名稱Emerging Technology and Architecture for Big-data Analytics讀者反饋學科排名
作者: 脫水 時間: 2025-3-21 23:29
Emerging Technology and Architecture for Big-data Analytics978-3-319-54840-1作者: 哥哥噴涌而出 時間: 2025-3-22 01:35 作者: 記憶法 時間: 2025-3-22 05:52 作者: 遠足 時間: 2025-3-22 12:10 作者: insipid 時間: 2025-3-22 15:17 作者: insipid 時間: 2025-3-22 17:55
https://doi.org/10.1007/978-3-322-96007-8r architectures resulting in a compact and energy-efficient “in-memory computing” platform. In this chapter, we will review spintronic device structures consisting of single-domain/domain-wall motion based devices for mimicking neuronal and synaptic units. System-level simulations indicate?～?100× im作者: Budget 時間: 2025-3-23 00:47 作者: 金桌活畫面 時間: 2025-3-23 04:16
its and systems;.Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics..978-3-319-85497-7978-3-319-54840-1作者: outrage 時間: 2025-3-23 07:40
Accelerating Data Analytics Kernels with Heterogeneous ComputingHLS-based exhaustive architectural exploration for implementation of the kernels practically infeasible. To address this challenge, we have developed Lin-Analyzer, a high-level accurate performance analysis tool that enables rapid design space exploration with various pragmas for FPGA-based accelera作者: 心胸狹窄 時間: 2025-3-23 12:02
Adaptive Dynamic Range Compression for Improving Envelope-Based Speech Perception: Implications for ts that were designed to evaluate the performance of the AEC strategy. In the first and second experiments, we investigated AEC performance under two types of challenging listening conditions: noisy and reverberant. In the third experiment, we explore the correlation between the adaptation rate usin作者: 小平面 時間: 2025-3-23 17:25 作者: V切開 時間: 2025-3-23 20:33 作者: Permanent 時間: 2025-3-23 23:26
In-Memory Data Compression Using ReRAMsnarios, such as data encryption and on-chip machine learning. This chapter explores the implementation of data compression algorithm using such an in-memory computing platform. We explain the building blocks of the in-memory computing architecture, the steps of a data compression algorithm and show 作者: Obstruction 時間: 2025-3-24 03:37 作者: geriatrician 時間: 2025-3-24 08:48
Notch Ligands for Lymphocyte Developmenteed much faster than their classical counterparts. However, in all the domains of computation, such improvements may not be available and also fabricating a commercial quantum computer is still elusive. We will try to briefly describe an outline of quantum paradigm in this material with possible implications in several aspects in data analytics.作者: 吵鬧 時間: 2025-3-24 12:30
Big Data Management in Neural Implants: The Neuromorphic Approachmes implies more processing in the implant but can provide compression factors from 10–10.. Lastly, a neuromorphic mixed-signal circuit to do intention decoding and provide maximum compression while dissipating sub-μW power is shown as a possible solution for neural implants of the future.作者: 宣稱 時間: 2025-3-24 17:23 作者: STAT 時間: 2025-3-24 22:37
Northern Irish Poetry and the Russian Turnugh a NUMA aware garbage collector. In this chapter, we use the LArge Memory Business Data Analytics (LAMBDA) workload to illustrate the importance of various scaling bottlenecks and to demonstrate the performance gain from the discussed solutions.作者: CLOUT 時間: 2025-3-24 23:57
Nostalgia Marketing and Consumer Behavior,oop: the definitive guide. O’Reilly Media, Sebastopol, 2009) have become primary datacenter applications, but the rise of massive data processing also has a major impact on the increasing demand for both datacenter computation and data processing in edge devices to improve scalability of massive sensing applications.作者: 大量殺死 時間: 2025-3-25 06:08 作者: 徹底檢查 時間: 2025-3-25 10:43
Scaling the Java Virtual Machine on a Many-Core Systemugh a NUMA aware garbage collector. In this chapter, we use the LArge Memory Business Data Analytics (LAMBDA) workload to illustrate the importance of various scaling bottlenecks and to demonstrate the performance gain from the discussed solutions.作者: acolyte 時間: 2025-3-25 13:15
New Solutions for Cross-Layer System-Level and High-Level Synthesisoop: the definitive guide. O’Reilly Media, Sebastopol, 2009) have become primary datacenter applications, but the rise of massive data processing also has a major impact on the increasing demand for both datacenter computation and data processing in edge devices to improve scalability of massive sensing applications.作者: morale 時間: 2025-3-25 18:21 作者: 熱情贊揚 時間: 2025-3-25 23:48 作者: OWL 時間: 2025-3-26 02:47
Side Channel Attacks and Their Low Overhead Countermeasures on Residue Number System Multipliersance, and are compatible to other countermeasures on both the logic level and the algorithm level. We prototype the proposed design on FPGA, and presented the implementation results confirm the efficiency of the proposed countermeasures.作者: 雜色 時間: 2025-3-26 07:41
Acceleration of MapReduce Framework on a Multicore Processoral results show that the MapReduce framework with hardware accelerators speeds up by 40 times at maximum compared to the pure software solution, and the proposed Topo-MapReduce speeds up further by 29% at maximum compared to the original MapReduce.作者: Chandelier 時間: 2025-3-26 09:40
ign abstraction and flow, from device, to circuits and syste.This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design a作者: jabber 時間: 2025-3-26 14:10 作者: 換話題 時間: 2025-3-26 20:50 作者: JOG 時間: 2025-3-26 22:23 作者: Chronological 時間: 2025-3-27 03:39
Compute-in-Memory Architecture for Data-Intensive Kernelschnology scaling are unlikely to sufficiently improve energy-efficiency. This chapter describes two embodiments of a novel and reconfigurable memory-based computing architecture which is designed to handle data-intensive kernels in a scalable and energy-efficient manner, suitable for next-generation systems.作者: Comedienne 時間: 2025-3-27 06:32
Ultra-Low-Power Biomedical Circuit Design and Optimization: Catching the Don’t Caresiomedical circuit design and optimization. The proposed framework seamlessly integrates data processing algorithms and their customized circuit implementations for co-optimization. The efficacy of the proposed framework is demonstrated by a case study of brain–computer interface (BCI).作者: RACE 時間: 2025-3-27 11:46 作者: 衣服 時間: 2025-3-27 13:38 作者: 變白 時間: 2025-3-27 19:36 作者: mercenary 時間: 2025-3-28 01:25 作者: Organonitrile 時間: 2025-3-28 02:35 作者: Sinus-Rhythm 時間: 2025-3-28 06:19
Least-squares-solver Based Machine Learning Accelerator for Real-time Data Analytics in Smart Buildi machine-learning accelerator for real-time data analytics in smart micro-grid of buildings. A compact yet fast incremental least-squares-solver based learning algorithm is developed on computational resource limited IoT hardware. The compact accelerator mapped on FPGA can perform real-time data ana作者: 圓木可阻礙 時間: 2025-3-28 13:17
Compute-in-Memory Architecture for Data-Intensive Kernelssing large datasets. These . kernels differentiate themselves from . kernels in that increased processor performance through parallel execution and technology scaling are unlikely to sufficiently improve energy-efficiency. This chapter describes two embodiments of a novel and reconfigurable memory-b作者: aesthetician 時間: 2025-3-28 18:22
New Solutions for Cross-Layer System-Level and High-Level Synthesisf big data and advanced data analytics that can effectively gather, analyze, generate insights about the data, and perform decision making. Data analytics allows analysis and optimization of massive datasets: deep analysis has led to advancements in business operations optimization, natural language作者: 減震 時間: 2025-3-28 22:14
Side Channel Attacks and Their Low Overhead Countermeasures on Residue Number System Multipliersblic-key cryptography. In this work, we examine the secure performance of RNS under side channel attacks, expose the vulnerabilities, and propose countermeasures accordingly. The proposed methods improve the resistance against side channel attacks without great area overhead or loss of speed perform作者: dowagers-hump 時間: 2025-3-29 01:18 作者: arthroplasty 時間: 2025-3-29 06:50 作者: 使絕緣 時間: 2025-3-29 09:57
Adaptive Dynamic Range Compression for Improving Envelope-Based Speech Perception: Implications for e to biological constraints, a compression scheme is required to adjust the wide dynamic range (DR) of input signals to a desirable level. Static envelope compression (SEC) is a well-known strategy used in CI speech processing, where a fixed compression ratio is adopted to narrow the envelope DR. Mo作者: output 時間: 2025-3-29 12:14 作者: licence 時間: 2025-3-29 15:52 作者: bibliophile 時間: 2025-3-29 21:52 作者: NOCT 時間: 2025-3-30 03:20
In-Memory Data Compression Using ReRAMstion of communication bandwidth and eventually helps to refine the data to provide information and knowledge. Given the growth of sensors and connected devices, the role of compression in data management is growing in importance steadily. Following the earliest computing abstractions, data is transf作者: 紅腫 時間: 2025-3-30 04:46
Big Data Management in Neural Implants: The Neuromorphic Approachuroprostheses. The tight power constraints of these systems prevent wireless data transmission of thousands of channels of neural activity. Hence extracting information from the raw data and transmitting just the compressed information is necessary for future implants. This chapter explores several 作者: detach 時間: 2025-3-30 08:59 作者: 繁殖 時間: 2025-3-30 13:47 作者: CEDE 時間: 2025-3-30 17:49
Northern Irish Poetry and the Russian Turnl machine need to be architected carefully to avoid single-thread bottlenecks. Among the solutions for challenges tackled in scaling a single Java Virtual Machine (JVM), we discuss the most rewarding ones. They include: converting shared data objects to per-thread independent objects, applying scala作者: oblique 時間: 2025-3-30 22:01
https://doi.org/10.1007/978-3-031-46285-6for efficient processing of compute-intensive data analytics kernels. In this chapter, we focus on the acceleration of data analytics kernels on heterogenous computing systems with FPGAs. The introduction of FPGAs in the context of data analytics is negatively impacted by the difficulty in programmi作者: 拋媚眼 時間: 2025-3-31 01:27
Successful Local Peacebuilding in Macedonia, machine-learning accelerator for real-time data analytics in smart micro-grid of buildings. A compact yet fast incremental least-squares-solver based learning algorithm is developed on computational resource limited IoT hardware. The compact accelerator mapped on FPGA can perform real-time data ana
