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Accelerating Machine-Learning

Machine-Learning in form of Deep Convolutional Neural Networks has demonstrated significant advantages over classical algorithms, however at the cost of a significant compute burdon. MLE has started putting together a set of accelerated platforms, solutions and focused services within the Xilinx FPGA ecosystem.

Currently, these accelerated platforms, solutions and services focus on the Inference phase of Deep-Learning. MLE's acceleration techniques for Deep Convolutional Neural Network Inference combine "unconventional" dataflow-oriented architectures with modern design flows using Xilinx High-Level Synthesis, and the Xilinx SDx tool chain.

Close collaboration with re-knowned experts from the Bavarian Multi-Media Lab at Augsburg University, Germany, facilitates rapid adoption of recent research results, for example, in the field of Reduced Precision Neural Networks.

MLE is a licensee of Xilinx and offers sub-licensing, technology support and complementary design services for integrating Accelerated Deep-Learning Inference into your application. When applied to the Deep-Learning Inference phase, Xilinx FPGA technology can provide a unique combination of low-latency response times, high compute performance in the Tera-OPS range, at very low Wattage.


  • Image processing and classification
  • Environment perception
  • Multi-camera object recognition systems
  • sensor fusion

Core Benefits

  • Very fast response time with low deterministic processing latencies
  • Very high, raw compute performance up to tens of Tera OPS
  • low power envelopes of typically less than 50 Watts
  • Scalability from embedded system, to High-Performance Compute (HPC)

Key Features

  • Highly integrated single-chip solutions
  • Scales to state-of-the art networks (CNV, ResNet-50, etc)


Accelerated Machine-Learning platforms and solutions are available for the following FPGA families:

Products Availability Matrix

Datasheets and Documentation

Please find below the User Guide for the Deep-Learning demo running on Amazon EC2 F1:

Please refer to these Technical Publications for first information regarding Acceleration of Machine-Learning: