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FPGA/GPU Cluster

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

cluster specifications

FPGA/GPU Cluster Software Stack

 

The FPGA/GPU cluster supports the three most commonly used deep learning frameworks, namely, TensorFlow, Caffe and MXNet. These frameworks provide a high-level abstraction layer for deep learning architecture specification, model training, tuning, testing and validation. Also included in the software stack are the various machine learning vendor specific libraries, that provide dedicated computing functions tuned for specific hardware architecture, delivering the best possible performance/power figure.

Cluster Software Stack

Contact:
Dr. Yassine Hariri
Senior Engineer, Platform Design
Phone: 613.530.4672
Email:
yassine.hariri@cmc.ca

Benefits

  • Secure remote access
  • Machine learning frameworks: Tensorflow, Caffe and MXNet
  • Support for deep learning training and inference
  • Customizability: Select the right combination of accelerators for your application
  • Reference designs using software stack for OpenCL, MPI heterogenous cluster computing
  • Scalability: Create one node neural network graph and scale up by using more nodes
  • Fast automated setup and configuration

Applications

  • Software IPs and applications targeting ML on heterogeneous computing systems (e.g. CNN, for object detection, speech recognition)
  • Software stack including: Parallel programming models, Compilers, Middleware, Runtime, Drivers and OSes
  • Case studies: ML, Big data analytics, data-intensive computing, cybersecurity
  • ASICs Prototyping: e.g., CMOS and other semiconductors, for implementing custom neural network accelerators

Resources

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