Event

Get moving with CMC FPGA/GPU Cluster

November 26, 2019 at 2:00 pm EST

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The FPGA/GPU cluster is a cloud-based compute infrastructure specifically designed to accelerate compute-intensive applications, such as machine learning training and inference, video and image processing, security, data analytics, bioinformatics, and financial computing. Latest state of the art acceleration technologies including Xilinx Alveo 200 FPGA and NVIDIA Tesla V100 GPU, closely coupled with server processors constitute the backbone of this cluster. The software stack consists of a complete ecosystem of machine learning frameworks, libraries as well as runtime targeting heterogeneous computing accelerators.

In this webinar, you will get a detailed walk-through of the hardware as well as the software architecture of the FPGA/GPU cluster, with instructions on how to reserve, access and develop applications targetting the different categories of nodes, namely Cerebro (2 FPGAs), Genisys (2 GPUs) and Synergy (1 FPGA and one GPU). A practical use case and a demo will be introduced, consisting of a complete machine learning training/inference flow of a convolutional neural network (CNN) architecture including:

  • Training and validation on the GPU V100.
  • Inference on the FPGA Alveo 200.

Register

Speaker

Dr. Yassine Hariri is a senior platform design engineer at CMC Microsystems with over 12 years of experience in implementing embedded as well as cloud-based heterogeneous computing platforms, with a focus on hardware architecture, software stack development and validation, design and mapping tools, as well as parallel programming models. He is currently leading projects related to the development, implementation and support of machine learning applications targeting heterogeneous computing systems. Dr. Hariri earned his B.A.Sc. in Computer Engineering from Ecole Marocaine des Sciences de l’ingénieur, Casablanca, Morroco, in 1998, and the M.S. and Ph.D. degrees from Ecole de Technologie Supérieure (ETS), Montreal, QC, Canada, in 2002 and 2008, respectively, all in electrical engineering.

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