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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.

The 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 three the 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. The software stack also includes 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

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

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

Contact Us

Dr. Yassine Hariri
Senior Staff Scientist
AI/ML and Embedded Systems
Office: 1.613.530.4672

YouTube Channel    Linkedin Group

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