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Machine Learning is a rapidly evolving technology that is re-inventing traditional applications and simultaneously inspiring new use models. These new applications deployed in the cloud and at on-premises Data Centers must perform in real-time, making it difficult for accelerators to keep pace.

Applications including Cloud Surveillance Analytics, Satellite Imaging, Bioinformatics, Financial Modeling, and Network Security require Machine Learning acceleration as well as other application-specific workloads involving OpenCV, Video Transcoding, and Smart NICs.

Product Highlights

Machine Learning Application running on Xilinx® Alveo™ Data Center accelerator cards
  • Delivers the highest real-time inference available with easy-to-use software tools to quickly deploy any ML application.
  • 4X Higher Real-Time Inference over High-End GPUs
  • 20X Higher Real-Time Inference over High-End CPUs
  • Easy-to-Use ML Frameworks, APIs, and Software
Product Highlights

Solution Overview

Machine Learning Application

ML Application enables developers to optimize and deploy accelerated ML inference. ML Application supports the most prevalent machine learning frameworks including Caffe, MxNet, and Tensorflow, as well as Python and RESTful APIs. 

ML Suite Components

xfDNN Compiler/Optimizer: auto-layer fusing, memory optimization, and framework integration

xfDNN Quantizer: Improves performance with auto model-precision INT8 calibration

Deployable on-premises or through cloud services


Xilinx Alveo™ Accelerator cards are capable of high-performance, energy-efficient real-time DNN inference.

An Alveo™ accelerator card running xDNN processing engines are capable of delivering more than 4,000 images per second of GoogLeNet v1 throughput at low latency without requiring batching.

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