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SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems [article]

Beidi Chen, Tharun Medini, James Farwell, Sameh Gobriel, Charlie Tai, Anshumali Shrivastava
2020 arXiv   pre-print
Deep Learning (DL) algorithms are the central focus of modern machine learning systems.  ...  We propose SLIDE (Sub-LInear Deep learning Engine) that uniquely blends smart randomized algorithms, with multi-core parallelism and workload optimization.  ...  ACKNOWLEDGEMENTS The work was supported by NSF-1652131, nsf-bigdata 1838177, AFOSR-YIPFA9550-18-1-0152, Amazon Research Award, and ONR BRC grant for Randomized Numerical Linear Algebra.  ... 
arXiv:1903.03129v2 fatcat:pbeouobwabehdmefvveage75vu

Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends

Mostafa Ahmed Ezzat, Mohamed A. Abd El Ghany, Sultan Almotairi, Mohammed A.-M. Salem
2021 Sensors  
Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems.  ...  The automation strategy of today's smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights.  ...  With more advancements in the area of Embedded Systems, Machine Learning engineers are now integrating large systems to undertake a specific job.  ... 
doi:10.3390/s21093222 pmid:34066509 fatcat:27lploodmvdl3k36x4jzw2acly

Distributed deep learning system for cancerous region detection on Sunway TaihuLight

GuoFeng Lv, MingFan Li, Hong An, Han Lin, Junshi Chen, Wenting Han, Qian Xiao, Fei Wang, Rongfen Lin
2020 CCF Transactions on High Performance Computing  
It reveals the great opportunity for joint combination of deep learning and HPC system.  ...  With a benchmark from deep learning-based cancerous region detection algorithm, the average parallel efficiency obtains over 80% for at most 1024 processors.  ...  The average parallel efficiency obtains over 80% for at most 2048 CGs, which demonstrates the great opportunity for joint of deep learning and HPC system.  ... 
doi:10.1007/s42514-020-00046-5 fatcat:4353jt2cprab7ij5v4rgnal6t4

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera, Maurizio Martina, Muhammad Shafique
2020 IEEE Access  
, explaining how to assess the quality of different networks and hardware systems designed for them.  ...  Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance.  ...  Moreover, it can efficiently scale over heterogeneous architectures to speed up the training process. ONNX [312] . This is not a framework but a representation format for deep learning models.  ... 
doi:10.1109/access.2020.3039858 fatcat:nticzqgrznftrcji4krhyjxudu

Hardware based Spatio-Temporal Neural Processing Backend for Imaging Sensors: Towards a Smart Camera [article]

Samiran Ganguly, Yunfei Gu, Mircea R. Stan, Avik W. Ghosh
2018 arXiv   pre-print
for smart cameras.  ...  We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors  ...  prototype Deep Learning accelerators that implement hardware architectures matching neural network designs at closeto-code level.  ... 
arXiv:1803.08635v1 fatcat:btfh4lpdmrh5fmdfrafcwgbdoq

Edge Intelligence: Architectures, Challenges, and Applications [article]

Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, Pan Hui
2020 arXiv   pre-print
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence.  ...  and deployed systems.  ...  ., a centralised solution for the small-scale system and a distributed solution for the large-scale system.  ... 
arXiv:2003.12172v2 fatcat:xbrylsvb7bey5idirunacux6pe

Program

2021 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)  
acceleration system usually consists of bus-connected micro-controllers, hardware accelerators, and external memory in order to support all the operations in various deep neural network (DNN) applications  ...  And use deep learning technology. This technology can easily add new diseases and pests for identification. In addition, combined with hardware accelerators.  ... 
doi:10.1109/icce-tw52618.2021.9602919 fatcat:aetmvxb7hfah7iuucbamos2wgu

Machine Learning for Security and the Internet of Things: the Good, the Bad, and the Ugly

Fan Liang, William G. Hatcher, Weixian Liao, Weichao Gao, Wei Yu
2019 IEEE Access  
The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and  ...  In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT.  ...  [167] carried out a large-scale investigation on how password managers could influence the real-world passwords of users. In addition, Hitaj et al.  ... 
doi:10.1109/access.2019.2948912 fatcat:wxd6imn62fgufdmfh3gtaijeru

Table of Contents

2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
Singh and Nan Ye .......... 241 A3DQN: Adaptive Anderson Acceleration for Deep Q-Networks Ripple Spreading Algorithm for Free-Flight Route Optimization in Dynamical Airspace Hang Zhou and Xiao-Bing Hu  ...  Classification Over a Large Corpus on Spark Jairson Rodrigues, Germano Vasconcelos and Paulo Maciel .......... 1702 CIBIM:Biometric Techniques and Systems/Machine Learning and AI in Biometrics and Identity  ... 
doi:10.1109/ssci47803.2020.9308155 fatcat:hyargfnk4vevpnooatlovxm4li

2022 Roadmap on Neuromorphic Computing and Engineering [article]

Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano (+47 others)
2022 arXiv   pre-print
built-in capabilities to learn or deal with complex data as our brain does.  ...  This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors.  ...  This new class of extremely low-power and lowlatency artificial intelligence systems could, In a world where power-hungry deep learning techniques are becoming a commodity, and at the same time, environmental  ... 
arXiv:2105.05956v3 fatcat:pqir5infojfpvdzdwgmwdhsdi4

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
Correlated Representation Learning for Nonlinear Batch Process Monitoring; TII April 2020 2839-2848 Jiang, S., see Li, Y., 1076-1085 Jiang, X., see Gong, K., 1625-1634 Jiang, X., see Xiao, J., TII April  ...  Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics; TII Jan. 2020 202-214 Imran, A., see Hussain, B., TII Aug. 2020 4986-4996 Imran, M., see Fu, S., TII Sept. 2020 6013-6022  ...  ., +, TII Dec. 2020 7521-7531 Distributed Robust Algorithm for Economic Dispatch in Smart Grids Over General Unbalanced Directed Networks.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

Guest Editorial Special Issue on 6G-Enabled Internet of Things

Qilian Liang, Tariq S. Durrani, Jing Liang, Jinhwan Koh, Xin Wang
2021 IEEE Internet of Things Journal  
In the field of IoT, sensors with large-scale perception ability are urgently needed for the scenarios, such as structure health monitoring, vehicle tracking, and so on.  ...  IoT data in sliding windows.  ... 
doi:10.1109/jiot.2021.3111457 fatcat:hs4beids7nh27mbz5ctz7mabvi

Table of contents

2017 2017 IEEE International Symposium on Circuits and Systems (ISCAS)  
for Oscillation-Free Performance of Digitally Controlled Converters W-124 -Improving EDP in Multi-Core Embedded Systems Through Multidimensional Frequency Scaling W-125 -Sliding-Mode Approach for  ...  Image-Guided Surgery O-3 -Live Demonstration: Event-Driven Real-Time Spoken Digit Recognition System O-4 -Live Demonstration: Hardware Implementation of Convolutional STDP for on-Line Visual Feature Learning  ... 
doi:10.1109/iscas.2017.8049750 fatcat:csazlovzq5g4bmzlf7uss65sy4

Sensor-Based Environmental Perception Technology for Intelligent Vehicles

Biyao Wang, Yi Han, Di Tian, Tian Guan, Haibin Lv
2021 Journal of Sensors  
and accelerate the landing process of intelligent vehicles.  ...  The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles.  ...  Various deep learning algorithms for self-driving cars are coming.  ... 
doi:10.1155/2021/8199361 fatcat:hw4m3ikkhfcstl33nxmdcdcxzu

Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review

Chinthakindi Balaram Murthy, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde, Zong Woo Geem
2020 Applied Sciences  
From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area.  ...  This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques.  ...  (OpenCL) [265] are utilized for GPU-accelerated embedded systems.  ... 
doi:10.3390/app10093280 fatcat:e6jrltv6lrhxjntlhq7d34247e
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