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Giorgia Fiscaletti, Marco Speziali,
Luca Stornaiuolo , Marco D. Santambrogio
CnvW2A2
• 6 Convolutional Layers
• 3 Fully-Connected Layers
[1] BNN-PYNQ Quantized Neural Network (QNN) on PYNQ](https://image.slidesharecdn.com/necsttalkrewards-200613174137/85/ReWArDS-NECSTTechTalk-11-06-2020-25-320.jpg)
Uploaded byNECST Lab @ Politecnico di Milano
ReWArDS - NECSTTechTalk 11/06/2020
The document discusses the use of reconfigurable hardware, such as FPGAs, for artificial intelligence and data science applications, emphasizing the need for performance, energy efficiency, and flexibility. It includes various research contexts involving hardware acceleration for big data processing, face detection, and runtime analysis in Python using Zynq SoCs. The findings highlight significant speed improvements in distributed systems compared to traditional CPU and GPU configurations.
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