Titlebook: Compact and Fast Machine Learning Accelerator for IoT Devices; Hantao Huang,Hao Yu Book 2019 Springer Nature Singapore Pte Ltd. 2019 Inter

HEM

書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices影響因子(影響力)




書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices影響因子(影響力)學(xué)科排名




書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices網(wǎng)絡(luò)公開度




書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices被引頻次




書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices被引頻次學(xué)科排名




書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices年度引用




書目名稱Compact and Fast Machine Learning Accelerator for IoT Devices年度引用學(xué)科排名




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

注意力集中

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

VEN

ALLEY

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

Intervention

Critical

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.

Critical

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

amputation

Sinus-Rhythm

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

公式