The Heterogeneous Computing Cluster (HCC) is a cloud-based, remotely accessible compute infrastructure specifically designed to build, train, and deploy machine learning models. Latest state of the art acceleration technologies including FPGAs, GP-GPUs and massively-parallel processing units, 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.
Heterogeneous Computing Cluster
Key Platform 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
- Greater flexibility for HW/SW exploration
- Scalability: Create one node neural network graph and scale up by using more nodes
- Fast automated setup and configuration
- Technical support and training from CMC Microsystems
- Larger system (more nodes) under development
- 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
HCC Software Stack
HCC 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.
HCC Software Stack
For more information, refer to the HCC Datasheet.
To access the HCC, please contact:
Senior Engineer, Platform Design
E-mail Yassine Hariri