机器学习设备
机器学习设备的解决方案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. 高性能织物连接和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. 机器学习设备的理想PCIe先进的结构开关解决方案应提供可扩展，低延迟和经济高效的多主机互连或GPU，NVME SSD和其他PCIe端点的网络。另一个重要的考虑因素是织物应用程序编程接口（API）的可用性，可以简化系统管理，大大减少了对多主机系统的市场和开发成本的时间。 探索我们的机器学习设备的投资组合：
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