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R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , “Techniques and systems for local independent failure domains”. 2017.
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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)
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Y. Bengio, “Practical recommendations for gradient-based training of deep architectures”, in Neural networks: Tricks of the trade, Springer, 2012, pp. 437–478.
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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.
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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)
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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.
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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.
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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)
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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)
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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.
Y. LeCun, Bengio, Y. , and Hinton, G. , “Deep learning”, Nature, vol. 521, pp. 436–444, 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)
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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.
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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.
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.
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.
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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)