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Book Chapter
Y. Bengio, Practical recommendations for gradient-based training of deep architectures, in Neural networks: Tricks of the trade, Springer, 2012, pp. 437–478.
Conference Proceedings
S. Biookaghazadeh, Zhao, M. , and Ren, F. , Are FPGAs Suitable for Edge Computing?, The USENIX Workshop on Hot Topics in Edge Computing (HotEdge '18). BOSTON, MA, 2018. (363.22 KB)
I. Hubara, Courbariaux, M. , Soudry, D. , El-Yaniv, R. , and Bengio, Y. , Binarized neural networks, Advances in neural information processing systems. pp. 4107–4115, 2016.
M. Courbariaux, Bengio, Y. , and David, J. - P. , Binaryconnect: Training deep neural networks with binary weights during propagations, Advances in Neural Information Processing Systems. pp. 3123–3131, 2015.
Z. Yu, Trindade, B. Machado, Green, M. , Zhang, Z. , Sneha, P. , Bank-Tavakoli, E. , Pawlowicz, C. , and Ren, F. , A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images, The 29th IEEE International Conference on Image Processing (ICIP). Bordeaux, France, 2022. (1.03 MB)
A. Mousavi, Patel, A. B. , and Baraniuk, R. G. , A deep learning approach to structured signal recovery, Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on. IEEE, pp. 1336–1343, 2015.
S. Biookaghazadeh, Xu, Y. , Zhou, S. , and Zhao, M. , Enabling scientific data storage and processing on big-data systems, Big Data (Big Data), 2015 IEEE International Conference on. IEEE, pp. 1978–1984, 2015. (966.13 KB)
E. Bank-Tavakoli, Riera, M. , Quraishi, M. Hassan, and Ren, F. , FSCHOL: An OpenCL-based HPC Framework for Accelerating Sparse Cholesky Factorization on FPGAs, The 33rd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). Virtual Event, pp. 209-220, 2021. (380.33 KB)
M. Riera, Bank-Tavakoli, E. , Quraishi, M. Hassan, and Ren, F. , HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC. Under Review.
S. Biookaghazadeh, Kaleido: Enabling Efficient Scientific Data Processing on Big-Data Systems, Networking, Architecture, and Storage (NAS), 2017 International Conference on. IEEE, pp. 1–10, 2017.
Journal Article
M. Courbariaux, Hubara, I. , Soudry, D. , El-Yaniv, R. , and Bengio, Y. , Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1, arXiv preprint arXiv:1602.02830, 2016.
S. Qaisar, Bilal, R. Muhammad, Iqbal, W. , Naureen, M. , and Lee, S. , Compressive sensing: From theory to applications, a survey, Journal of Communications and networks, vol. 15, pp. 443–456, 2013.
Y. LeCun, Bengio, Y. , and Hinton, G. , Deep learning, Nature, vol. 521, pp. 436–444, 2015.
M. Mehdi Ghahremanpour, Arab, S. Shahriar, Biookaghazadeh, S. , Zhang, J. , and van der Spoel, D. , MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program, Bioinformatics, vol. 30, pp. 439–441, 2013. (192.44 KB)
C. Hegde, Sankaranarayanan, A. C. , Yin, W. , and Baraniuk, R. G. , NuMax: A convex approach for learning near-isometric linear embeddings, IEEE Transactions on Signal Processing, vol. 63, pp. 6109–6121, 2015.
M. Hassan Quraishi, Bank-Tavakoli, E. , and Ren, F. , A Survey of System Architectures and Techniques for FPGA Virtualization, IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 9, pp. 2216-2230, 2021. (435.22 KB)
R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , Techniques and systems for local independent failure domains. 2017.