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Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications.
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J. Zhang and J. Li, “Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network,” in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays , ser. FPGA ’17. New York, NY, USA: ACM, 2017, pp. 25–34. [Online]. Available: http://doi.acm.org/10.1145/3020078.3021698
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U. Aydonat, S. O’Connell, D. Capalija, A. C. Ling, and G. R. Chiu, “An OpenCL \ \backslash texttrademark Deep Learning Accelerator on Arria 10,” in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays , ser. FPGA ’17. New York, NY, USA: ACM, 2017, pp. 55–64. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3020078.3021738 http://doi.acm.org/10.1145/3020078.3021738
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2017
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D. J. M. Moss, E. Nurvitadhi, J. Sim, A. Mishra, D. Marr, S. Subhaschandra, and P. H. W. Leong, “High performance binary neural networks on the Xeon+FPGA™ platform,” in 2017 27th International Conference on Field Programmable Logic and Applications (FPL) . IEEE, sep 2017, pp. 1–4. [Online]. Available: http://ieeexplore.ieee.org/document/8056823/
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A. Rodriguez, “Intel Processors for Deep Learning Training,” nov 2017. [Online]. Available: https://software.intel.com/en-us/articles/intel-processors-for-deep-learning-training
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D. Lacey, “Preliminary IPU Benchmarks,” oct 2017. [Online]. Available: https://www.graphcore.ai/posts/preliminary-ipu-benchmarks-providing-previously-unseen-performance-for-a-range-of-machine-learning-applications
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D. Tenenbaum, “As computing moves to cloud, UW-Madison spinoff offers faster, cleaner chip for data centers,” may 2017. [Online]. Available: https://news.wisc.edu/as-computing-moves-to-cloud-uw-madison-spinoff-offers-faster-cleaner-chip-for-data-centers/
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J. L. Hennessy and D. A. Patterson, “A New Golden Age for Computer Architecture,” Communications of the ACM , vol. 62, no. 2, pp. 48–60, jan 2019. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3310134.3282307
2019
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