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Integrating GPUs and Deep Learning Accelerators for Efficient Processing

Agbaje M.O

Abstract


Equipment utilized in executing counterfeit brain networks is essential as it plays a significant part to play in the speed and productivity of the entire framework. It is likewise an expressed reality that the man-made brainpower industry is at junction for which processor (Profound Learning Gas pedals and Illustrations Handling Unit) best fits the portfolio for the most integral asset for profound learning. In this article, we directed a similar report on the two processors by featuring their selling focuses and passes and presented a defense for the two processors to cooperate in a framework, where one processor covers the slip by of the other one to upgrade effective figuring.


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Nurvitadhi, E., Venkatesh, G., Sim, J., Marr, D., Huang, R., Ong Gee Hock, J., ... & Boudoukh, G. (2017, February). Can FPGAs beat GPUs in accelerating next-generation deep neural networks?. In Proceedings of the 2017 ACM/SIGDA international symposium on field-programmable gate arrays (pp. 5-14).

Bolz, J., Farmer, I., Grinspun, E., & Schröder, P. (2003). Sparse matrix solvers on the GPU: conjugate gradients and multigrid. ACM transactions on graphics (TOG), 22(3), 917-924.

Harris, M. J., Baxter, W. V., Scheuermann, T., & Lastra, A. (2003, July). Simulation of cloud dynamics on graphics hardware. In Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware (pp. 92-101).

conference on Graphics hardware (pp. 92-101).

Krüger, J., & Westermann, R. (2005). Linear algebra operators for GPU implementation of numerical algorithms. In ACM SIGGRAPH 2005 Courses (pp. 234-es).

Nurvitadhi, E., Venkatesh, G., Sim, J., Marr, D., Huang, R., Ong Gee Hock, J., ... & Boudoukh, G. (2017, February). Can FPGAs beat GPUs in accelerating next-generation deep neural networks?. In Proceedings of the 2017 ACM/SIGDA international symposium on field-programmable gate arrays (pp. 5-14).

Bengio, Y., Boulanger-Lewandowski, N., & Pascanu, R. (2013, May). Advances in optimizing recurrent networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8624-8628). IEEE.

Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013, May). Improving deep neural networks for LVCSR using rectified linear units and dropout. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8609-8613). IEEE.

Chilimbi, T., Suzue, Y., Apacible, J., & Kalyanaraman, K. (2014). Project adam: Building an efficient and scalable deep learning training system. In 11th USENIX symposium on operating systems design and implementation (OSDI 14) (pp. 571-582).


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