Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Learning?

Linda Barney , Mar. 21, 2017 – 

Continued exponential growth of digital data of images, videos, and speech from sources such as social media and the internet-of-things is driving the need for analytics to make that data understandable and actionable.

Data analytics often rely on machine learning (ML) algorithms. Among ML algorithms, deep convolutional neural networks (DNNs) offer state-of-the-art accuracies for important image classification tasks and are becoming widely adopted.

At the recent International Symposium on Field Programmable Gate Arrays (ISFPGA), Dr. Eriko Nurvitadhi from Intel Accelerator Architecture Lab (AAL), presented research on Can FPGAs beat GPUs in Accelerating Next-Generation Deep Neural Networks. Their research evaluates emerging DNN algorithms on two generations of Intel FPGAs (Intel Arria10 and Intel Stratix 10) against the latest highest performance NVIDIA Titan X Pascal* Graphics Processing Unit (GPU).

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