CNN-Oriented Placement Algorithm for High-Performance Accelerators on Rad-Hard FPGAs
Luca Sterpone,
Sarah Azimi,
Corrado De Sio
Abstract:Convolutional Neural Networks (CNNs) are quickly becoming one of the most common applications running on hardware accelerators. Considering Field Programmable Gate Arrays (FPGAs), due to their high flexibility and computational performance, they are suitable for fast classification tasks and therefore, pave the way for new machine learning inference approaches. In this work, we first designed a fully interconnected CNN architecture implementable on a single FPGA. Secondly, we developed a new Neural Node-orient… Show more
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