Abstract
Convolutional Neural Networks (CNNs) have applications in many valuable domains such as object detection for autonomous cars and security using facial recognition. This vast field of application usually places strict non-functional requirements such as resource-efficient implementations on the hardware devices, while at the same time requiring flexibility. In response, this work presents a C++-based software library of reusable modules to build arbitrary CNNs that support High-Level-Synthesis to be implemented as FPGA hardware accelerators for the inference process. Our work demonstrates how parametrization and modularization of basic building blocks of a CNN enable easier customization of the hardware to match the software model. This project also works with low-precision parameters throughout the CNN to provide a more resource-efficient implementation.
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Hernandez, H.G.M., Mahmood, S., Brandalero, M., Hübner, M. (2020). A Modular Software Library for Effective High Level Synthesis of Convolutional Neural Networks. In: Rincón, F., Barba, J., So, H., Diniz, P., Caba, J. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science(), vol 12083. Springer, Cham. https://doi.org/10.1007/978-3-030-44534-8_16
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