Can FPGAs Beat GPUs in Accelerating Next-Generation Deep ...

Can FPGAs Beat gpus in Accelerating Next-Generation deep Neural Networks? Eriko Nurvitadhi1, Ganesh Venkatesh1, Jaewoong Sim1, Debbie Marr1, Randy Huang2, Jason Gee Hock Ong2, Yeong Tat Liew2, Krishnan Srivatsan3, Duncan Moss3, Suchit Subhaschandra3, Guy Boudoukh4 1 Accelerator Architecture Lab, 2 Programmable Solutions Group, 3 FPGA Product Team, 4 Computer Vision Group Intel Corporation ABSTRACT Current- generation deep Neural Networks (DNNs), such as AlexNet and VGG, rely heavily on dense floating-point matrix multiplication (GEMM), which maps well to gpus (regular parallelism, high TFLOP/s). Because of this, gpus are widely used for Accelerating DNNs. Current FPGAs offer superior energy efficiency (Ops/Watt), but they do not offer the performance of today s gpus on DNNs. In this paper, we look at upcoming FPGA technology advances, the rapid pace of innovation in DNN algorithms, and consider whether future high-performance FPGAs will outperform gpus for Next-Generation DNNs.

Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? Eriko Nurvitadhi1, Ganesh Venkatesh1, Jaewoong Sim1, Debbie Marr1, Randy Huang2, Jason Gee Hock Ong2, Yeong Tat Liew2, Krishnan Srivatsan3, Duncan Moss3, Suchit Subhaschandra3, Guy Boudoukh4 1Accelerator Architecture Lab, 2Programmable Solutions Group, 3FPGA Product Team, 4Computer Vision Group

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  Next, Generation, Deep, Accelerating, Gpus, Gpus in accelerating next generation deep

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