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Acceleration of Deep Learning on a FPGA

Modern machine learning methods based on deep neural networks commonly rely on hardware acceleration of critical computational kernels in order to improve performance and reduce energy consumption. Our project explores this new approach by mapping a convolutional neural network onto an FPGA to classify images faster than a traditional CPU-only implementation.

Team Members: 

Nafis Akbar

Christian Han

Nick Morin

Ghoshank Patel