Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Learning?
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 …
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Learning? was written by Nicole Hemsoth at The Next Platform.
Recently, we had a customer challenge our team to prove to them the operational gains and demonstrate the cross-functional tooling VMware provides to assist them in scaling from zero to hundreds of VMs on the platform. Our goal was simple – exhibit a complete lifecycle for any customer to go from evaluation to production operation...
AI allows IoT to turn insight into action.