標(biāo)題: Titlebook: Compact and Fast Machine Learning Accelerator for IoT Devices; Hantao Huang,Hao Yu Book 2019 Springer Nature Singapore Pte Ltd. 2019 Inter [打印本頁] 作者: HEM 時間: 2025-3-21 16:12
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書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices讀者反饋
書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices讀者反饋學(xué)科排名
作者: 注意力集中 時間: 2025-3-21 21:54
Fundamentals and Literature Review,e use IoT based smart buildings as one example to illustrate the edge computing in IoT system for applications such as indoor positioning, energy management and network intrusion detection. Furthermore, we will discuss the basics of the machine learning algorithms, distributed machine learning, mach作者: VEN 時間: 2025-3-22 02:53 作者: ALLEY 時間: 2025-3-22 08:24
Tensor-Solver for Deep Neural Network, dimensional tensors with low-rank decomposition, significant neural network compression can be achieved with maintained accuracy. A layer-wise training algorithm of tensorized multilayer neural network is further introduced by modified alternating least-squares (MALS) method. The proposed TNN algor作者: Intervention 時間: 2025-3-22 12:16 作者: Critical 時間: 2025-3-22 15:23
Conclusion and Future Works,al-world, use sensed information to reason the environment and then perform the desired action. The intelligence of IoT systems comes from appropriate actions by reasoning the environmental data, which is mainly based on machine learning techniques.作者: Critical 時間: 2025-3-22 20:17
Steffen Sigmund,Gert Albert,Mateusz Stachuradiscuss the machine learning based data analytics techniques from both the algorithm perspective and computation perspective. As the increasing complexity of machine learning algorithms, there is an emerging need to re-examine the current computation platform. A dedicated hardware computation platfo作者: amputation 時間: 2025-3-23 00:45 作者: Sinus-Rhythm 時間: 2025-3-23 03:00
Steffen Sigmund,Gert Albert,Mateusz Stachuraapplied to reduce the training complexity. Furthermore, the optimized learning algorithm is mapped on CMOS and RRAM based hardware. The two implementations on both RRAM and CMOS are presented. The detailed analysis of hardware implementation is discussed with significant speed-up and energy-efficien作者: 公式 時間: 2025-3-23 08:26 作者: 逃避系列單詞 時間: 2025-3-23 10:51
Marie-Luise Klein,Angela Deitersen-Wieberically, this chapter investigates three IoT applications, which are indoor positioning system, energy management system and network intrusion detection system. The proposed distributed-solver can perform real-time data analytics on smart gateways with consideration of constrained computation resourc作者: 債務(wù) 時間: 2025-3-23 14:18
Mein Weg zu und mit Paul Feyerabend,al-world, use sensed information to reason the environment and then perform the desired action. The intelligence of IoT systems comes from appropriate actions by reasoning the environmental data, which is mainly based on machine learning techniques.作者: 人類學(xué)家 時間: 2025-3-23 19:12
Hantao Huang,Hao YuOffers readers a systematic and comprehensive literature review of fast and compact machine learning algorithms on IoT devices.Provides various techniques on neural network model optimization such as 作者: 神秘 時間: 2025-3-24 02:08
Computer Architecture and Design Methodologieshttp://image.papertrans.cn/c/image/230804.jpg作者: 沉思的魚 時間: 2025-3-24 03:06
Compact and Fast Machine Learning Accelerator for IoT Devices978-981-13-3323-1Series ISSN 2367-3478 Series E-ISSN 2367-3486 作者: 同義聯(lián)想法 時間: 2025-3-24 08:32 作者: 殘忍 時間: 2025-3-24 13:46 作者: Hemiplegia 時間: 2025-3-24 18:46 作者: debouch 時間: 2025-3-24 22:45
Springer Nature Singapore Pte Ltd. 2019作者: aesthetician 時間: 2025-3-24 23:21
2367-3478 -Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings..978-981-13-3323-1Series ISSN 2367-3478 Series E-ISSN 2367-3486 作者: Admonish 時間: 2025-3-25 03:59
Compact and Fast Machine Learning Accelerator for IoT Devices作者: 盡管 時間: 2025-3-25 07:56
Book 2019verage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings..作者: Foregery 時間: 2025-3-25 15:15
Marie-Luise Klein,Angela Deitersen-Wieberd by fine-tuning with backward propagation (BP). Significant compression rate can be achieved for MNIST dataset and CIFAR-10 dataset. In addition, a 3D multi-layer CMOS-RRAM accelerator architecture is proposed for energy-efficient and highly-parallel computation (Figures and illustrations may be reproduced from [.,.,.]).作者: conservative 時間: 2025-3-25 16:25
Marie-Luise Klein,Angela Deitersen-Wieberteway networks, which is desirable to build a real cyber-physical system towards future smart home, smart building, smart community and further a smart city (Figures and illustrations may be reproduced from [., .,.,., .]).作者: 極深 時間: 2025-3-25 20:25 作者: SEED 時間: 2025-3-26 03:25
Distributed-Solver for Networked Neural Network,teway networks, which is desirable to build a real cyber-physical system towards future smart home, smart building, smart community and further a smart city (Figures and illustrations may be reproduced from [., .,.,., .]).作者: 擔(dān)心 時間: 2025-3-26 07:46
Book 2019etwork compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with co作者: MILL 時間: 2025-3-26 10:32 作者: Junction 時間: 2025-3-26 14:28 作者: Pulmonary-Veins 時間: 2025-3-26 17:10 作者: 書法 時間: 2025-3-26 21:03
Steffen Sigmund,Gert Albert,Mateusz Stachurations on both RRAM and CMOS are presented. The detailed analysis of hardware implementation is discussed with significant speed-up and energy-efficiency improvement when compared with CPU and GPU based implementations (Figures and illustrations may be reproduced from [., .]).作者: DOSE 時間: 2025-3-27 04:15
Introduction,xity of machine learning algorithms, there is an emerging need to re-examine the current computation platform. A dedicated hardware computation platform becomes a solution of IoT systems. We further discuss the hardware computation platform on both CMOS and RRAM technology.作者: Dignant 時間: 2025-3-27 08:27
Fundamentals and Literature Review,gement and network intrusion detection. Furthermore, we will discuss the basics of the machine learning algorithms, distributed machine learning, machine learning accelerators and machine learning model optimizations. A comprehensive literature review on distributed and compact machine learning algorithms is also provided.作者: anaphylaxis 時間: 2025-3-27 13:18
Least-Squares-Solver for Shallow Neural Network,tions on both RRAM and CMOS are presented. The detailed analysis of hardware implementation is discussed with significant speed-up and energy-efficiency improvement when compared with CPU and GPU based implementations (Figures and illustrations may be reproduced from [., .]).作者: Popcorn 時間: 2025-3-27 15:46 作者: 有法律效應(yīng) 時間: 2025-3-27 21:46 作者: gait-cycle 時間: 2025-3-27 23:30
2211-7644 e hand, and the arrival of yersinia pestis – plague through ports on the Mediterranean at regular intervals of several years, on the other, mortality became a foreseeable event..978-3-319-44367-6978-3-319-44368-3Series ISSN 2211-7644 Series E-ISSN 2211-7652 作者: ascetic 時間: 2025-3-28 02:32
