Accelerating Deep Convolutional Neural Networks Using Specialized Hardware

Kalin Ovtcharov, Olatunji Ruwase, Joo-Young Kim, Jeremy Fowers, Karin Strauss, Eric S. Chung
Microsoft Research
Microsoft Research, 2015


   author={Kalin Ovtcharov and Olatunji Ruwase and Joo-Young Kim and Jeremy Fowers and Karin Strauss and Eric S. Chung},


   title={Accelerating Deep Convolutional Neural Networks Using Specialized Hardware},




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Recent breakthroughs in the development of multi-layer convolutional neural networks have led to stateof-the-art improvements in the accuracy of non-trivial recognition tasks such as large-category image classification and automatic speech recognition [1]. These many-layered neural networks are large, complex, and require substantial computing resources to train and evaluate [2]. Unfortunately, these demands come at an inopportune moment due to the recent slowing of gains in commodity processor performance. Hardware specialization in the form of GPGPUs, FPGAs, and ASICs1 offers a promising path towards major leaps in processing capability while achieving high energy efficiency. To harness specialization, an effort is underway at Microsoft to accelerate Deep Convolutional Neural Networks (CNN) using servers augmented with FPGAs-similar to the hardware that is being integrated into some of Microsoft’s datacenters [3]. Initial efforts to implement a single-node CNN accelerator on a mid-range FPGA show significant promise, resulting in respectable performance relative to prior FPGA designs and high-end GPGPUs, at a fraction of the power. In the future, combining multiple FPGAs over a low-latency communication fabric offers further opportunity to train and evaluate models of unprecedented size and quality.
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