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FPGA-Accelerated Deep-Learning Inference with Binarized Neural Networks

FPGA-Accelerated Deep-Learning Inference with Binarized Neural Networks

By: MLE Latest Version: 1.2

This version has been removed and is no longer available to new customers.

Product Overview

Image classification of the Cifar10 dataset using the CNV neural network. Based on Xilinx public proof-of-concept implementation of a reduced-precision, Binarized Neural Network (BNN) implemented in FPGA, MLE developed this demo to showcase the performance benefits of Deep-Learning Inference when running on AWS F1. Starting point for this demo was PYNQ-BNN from Xilinx [] using the so-called FINN framework [] from Y. Umuroglu et al. Several changes were applied during porting on AWS F1: modifications to the PYNQ-BNN software library including Python, and design-flow adjustments to support the AWS F1 SDAccel workflow.

This demo is especially intended for researchers and developers interested in FPGA-based acceleration in general, and in accelerated reduced-precision Neural Network inference with FPGA in particular.

For further information or for Amazon AWS F1 Design Services please contact us at





Operating System

Linux/Unix, Ubuntu 16.04

Delivery Methods

  • Amazon Machine Image

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