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Radar Signal Processing for Sensing in Assisted Living: The challenges associated with real-time implementation of emerging algorithms

Julien Le Kernec, Francesco Fioranelli, Chuanwei Ding, Heng Zhao, Li Sun, Hong Hong, Jordane Lorandel, Olivier Romain
2019 IEEE Signal Processing Magazine  
This is covered through 3 example applications: human activity recognition for activities of daily living, respiratory disorder and Sleep Stages classification.  ...  O. (2019) Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms.  ...  FPGAs also have high performance DSP blocks and floating-point units, delivering much more processing power than conventional CPUs.  ... 
doi:10.1109/msp.2019.2903715 fatcat:vifhwthbnzhmzf4gc6pbta2gyi

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
., TCSVT Dec. 2020 4944-4952 Floating point arithmetic Heterogeneous Acceleration of HAR Applications.  ...  ., +, TCSVT Dec. 2020 4496-4512 Action Recognition Scheme Based on Skeleton Representation With DS-LSTM Network.  ...  A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs [article]

Paolo Meloni, Alessandro Capotondi, Gianfranco Deriu, Michele Brian, Francesco Conti, Davide Rossi, Luigi Raffo, Luca Benini
2017 arXiv   pre-print
Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition.  ...  This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs.  ...  Originally designed for the inference task, and given the importance of the learning, Google announced a second, more flexible version supporting floating point operations, also suitable for training of  ... 
arXiv:1712.00994v1 fatcat:s2e2eaafpbcffnutlzktggcooe

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
Human Actions in Video; TCSVT March 2019 800-814 Tu, Z., Xie, W., Dauwels, J., Li, B., and Yuan, J., Semantic Cues Enhanced Multimodality Multistream CNN for Action Recognition; TCSVT May 2019 1423-1437  ...  ., TCSVT Feb. 2019 375-389 Discriminative Spatio-Temporal Pattern Discovery for 3D Action Recognition.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

NEURAghe

Paolo Meloni, Alessandro Capotondi, Gianfranco Deriu, Michele Brian, Francesco Conti, Davide Rossi, Luigi Raffo, Luca Benini
2018 ACM Transactions on Reconfigurable Technology and Systems  
Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition.  ...  This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs.  ...  Originally designed for the inference task, and given the importance of the learning, Google announced a second, more flexible version supporting floating point operations, also suitable for training of  ... 
doi:10.1145/3284357 fatcat:nsc2gkpjdbbghmz6wcbrwqhkla

The M2DC Project: Modular Microserver DataCentre

Mariano Cecowski, Giovanni Agosta, Ariel Oleksiak, Michal Kierzynka, Micha vor dem Berge, Wolfgang Christmann, Stefan Krupop, Mario Porrmann, Jens Hagemeyer, Rene Griessl, Meysam Peykanu, Lennart Tigges (+13 others)
2016 2016 Euromicro Conference on Digital System Design (DSD)  
The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware.  ...  This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs.  ...  or GPGPUs, although floating point arithmetic is rather complex to implement, vendor and third party tools offer configurable soft macros of various floating point computations.  ... 
doi:10.1109/dsd.2016.76 dblp:conf/dsd/CecowskiAOKBCKP16 fatcat:bu4nbkqaejebjafrotibui6mkq

An OpenCL(TM) Deep Learning Accelerator on Arria 10 [article]

Utku Aydonat, Shane O'Connell, Davor Capalija, Andrew C. Ling, Gordon R. Chiu
2017 arXiv   pre-print
This comes to 1382 GFLOPs and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the state-of-the-art on FPGAs.  ...  Convolutional neural nets (CNNs) have become a practical means to perform vision tasks, particularly in the area of image classification.  ...  ACKNOWLEDGEMENTS We would like to thank Stephen Weston for his insightful comments and Kevin Jin for the experimental data.  ... 
arXiv:1701.03534v1 fatcat:fivnwoibxjbphdg3veuutfdjoy

A Survey of Deep Learning-based Object Detection

Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu
2019 IEEE Access  
With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved.  ...  Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art  ...  Second, point-cloud based methods project point clouds into a 2D image to process or generate a 3D representation of the point cloud directly in a voxel structure, where the former loses information and  ... 
doi:10.1109/access.2019.2939201 fatcat:jesz2av2tjbkxfpaqyecptgls4

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Shibo Zhang, Yaxuan Li, Shen Zhang, Farzad Shahabi, Stephen Xia, Yu Deng, Nabil Alshurafa
2022 Sensors  
Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR).  ...  This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing  ...  Acknowledgments: Special thanks to Haik Kalamtarian and Krystina Neuman for their valuable feedback. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22041476 pmid:35214377 pmcid:PMC8879042 fatcat:vp6jssypezbd5cnyzn4g35eqrm

Design of Embedded Augmented Reality Systems [chapter]

J. Toledo, J. J., J. Garrigs, R. Toledo-Moreo, J. M.
2010 Augmented Reality  
The description of its main processing cores for video acquisition and processing, for hand recognition, for the user interface, etc. and the evaluation of their performances highlight the advantages of  ...  Finally, the chapter is completed with an example which illustrates the advantages of the FPGA-based approach as platform for developing AR applications: a portable real time system for helping visually  ...  • The algorithm should use integer or fixed point arithmetic when possible, minimizing the inference of floating point units that reduce the processing speed and devour FPGA resources.  ... 
doi:10.5772/7126 fatcat:gvhp4cbrg5ao3n2qbyj2cnypfu

Artificial neural networks in hardware: A survey of two decades of progress

Janardan Misra, Indranil Saha
2010 Neurocomputing  
We specifically discuss, in detail, neuromorphic designs including spiking neural network hardware, cellular neural network implementations, reconfigurable FPGA based implementations, in particular, for  ...  HNN research has witnessed a steady progress for more than last two decades, though commercial adoption of the technology has been relatively slower.  ...  Based upon the purpose of reconfiguration (prototyping and simulation, density enhancement, and topology adaptation) as well as data representation techniques (integer, floating point, and bit stream arithmetic  ... 
doi:10.1016/j.neucom.2010.03.021 fatcat:regzu6sshvekzd5wxcuaiytgqu

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie, Laith Farhan
2021 Journal of Big Data  
It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with  ...  by human performance.  ...  Acknowledgements We would like to thank the professors from the Queensland University of Technology and the University of Information Technology and Communications who gave their feedback on the paper.  ... 
doi:10.1186/s40537-021-00444-8 pmid:33816053 pmcid:PMC8010506 fatcat:x2h5qs5c2jbntipu7oi5hfnb6u

Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments [article]

Manish Bhattarai
2021 arXiv   pre-print
Next, we extended this CNN framework for object detection, tracking, segmentation with a Mask RCNN framework, and scene description with a multimodal natural language processing(NLP) framework.  ...  With these ad-hoc deep learning structures, we built the artificial intelligence system's backbone for firefighters' situational awareness.  ...  Illustration of word prediciton . . . . . . . . . . . . . . . . . . . . . The system comprises a CNN block for visual feature extraction. The inputs of the CNN block are image datasets.  ... 
arXiv:2107.11043v2 fatcat:3jm5zawelze7dhx37luja7mly4

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications [article]

Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian, Melika Payvand, Elisa Donati, Bernabe Linares-Barranco, Giacomo Indiveri
2020 arXiv   pre-print
With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and  ...  This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices.  ...  For our acceleration, we use fixed-point parameter representations on a Starter Platform for OpenVINO Toolkit FPGA using OpenCL.  ... 
arXiv:2007.05657v1 fatcat:amqutl3suvgq5nygna4ef36usy

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  
In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL  ...  This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much  ...  It is, therefore, possible to move from the floating-point representation to a shorter fixed-point representation (see Figure 44 ).  ... 
doi:10.1109/access.2020.3039858 fatcat:nticzqgrznftrcji4krhyjxudu
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