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The Metis AIPU PCIe AI Accelerator Card provides an unparalleled performance boost for AI tasks by leveraging multiple Metis AIPUs within a single setup. This card is capable of delivering up to 856 TOPS, supporting complex AI workloads such as computer vision applications that require rapid and efficient data processing. Its design allows for handling both small-scale and extensive applications with ease, ensuring versatility across different scenarios. By utilizing a range of deep learning models, including YOLOv5 and ResNet-50, this AI accelerator card processes up to 12,800 FPS for ResNet-50 and an impressive 38,884 FPS for MobileNet V2-1.0. The card’s architecture enables high throughput, making it particularly suited for video analytics tasks where speed is crucial. The card also excels in scenarios that demand high energy efficiency, providing best-in-class performance at a significantly reduced operational cost. Coupled with the Voyager SDK, the Metis PCIe card integrates seamlessly into existing AI systems, enhancing development speed and deployment efficiency.
The Metis AIPU M.2 Accelerator Module is a powerful AI processing solution designed for edge devices. It offers a compact design tailored for applications requiring efficient AI computations with minimized power consumption. With a focus on video analytics and other high-demand tasks, this module transforms edge devices into AI-capable systems. Equipped with the Metis AIPU, the M.2 module can achieve up to 3,200 FPS for ResNet-50, providing remarkable performance metrics for its size. This makes it ideal for deployment in environments where space and power availability are limited but computational demands are high. It features an NGFF (Next Generation Form Factor) socket, ensuring it can be easily integrated into a variety of systems. The module leverages Axelera's Digital-In-Memory-Computing technology to enhance neural network inference speed while maintaining power efficiency. It's particularly well-suited for applications such as multi-channel video analytics, offering robust support for various machine learning frameworks, including PyTorch, ONNX, and TensorFlow.
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