Machine Learning Appliances
Solutions for Machine Learning Appliances
The GPU machine learning market is rapidly evolving, with advanced technologies like low power consumption, high throughput and flexibility accelerating the adoption of machine learning applications worldwide. As the use of GPUs for deep learning, artificial intelligence and machine learning enable radical advances in areas of image classification, speech recognition, autonomous driving, bioinformatics and video analytics, the need for efficient parallel computing continues to grow.
High-performance fabric connectivity and composability for multi-host GPU and NVMe SSD systems is critical to ensure dynamic allocation of GPU resources to match workload requirements and maximize system efficiency. Microsemi’s Switchtec PAX switches, for example, feature dynamic partitioning and multi-host SR-IOV sharing, enabling real-time “composition” or dynamic allocation of GPU resources to a specific host or set of hosts using standard host drives.
The ideal PCIe advanced fabric switch solutions for machine learning appliances should deliver a scalable, low-latency and cost-effective multi-host interconnect or a network of GPUs, NVMe SSDs and other PCIe endpoints. Another important consideration is the availability of a fabric application programming interface (API), which can simplify system management, greatly reducing both time to market and development cost for multi-host systems.
Explore our portfolio for machine learning appliances: