Abstract
This paper presents an implementation of a Convolutional Neural Network (CNN) algorithm using a linear array of full mesh dynamically and partially reconfigurable Coarse Grained Reconfigurable Arrays (CGRAs). Accelerating CNNs using GPUs and FPGAs is more common and there are few works that address the topic of CNN acceleration using CGRAs. Using CGRAs can bring size and power advantages compared to GPUs and FPGAs. The contribution of this paper is to study the performance of full mesh dynamically and partially reconfigurable CGRAs for CNN acceleration. The CGRA used is an improved version of the previously published Versat CGRA, adding multi CGRA core support and pre-silicon configurability. The results show that the proposed CGRA is as easy to program as the original full mesh Versat CGRA, and that its performance and power consumption scale linearly with the number of instances.
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Acknowledgments
This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) under projects PTDC/EEI-HAC/30848/2017 and UIDB/50021/2020.
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Mário, V., Lopes, J.D., Véstias, M., de Sousa, J.T. (2020). Implementing CNNs Using a Linear Array of Full Mesh CGRAs. 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_22
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