Publication: Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification
Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification
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Linares-Barranco, A., Rios-Navarro, A., Tapiador-Morales, R., & Delbruck, T. (2019). Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification (ArXiv.Org). https://arxiv.org/abs/1905.07419
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Deep-learning is a cutting edge theory that is being applied to many fields. For vision applications the Convolutional Neural Networks (CNN) are demanding significant accuracy for classification tasks. Numerous hardware accelerators have populated during the last years to improve CPU or GPU based solutions. This technology is commonly prototyped and tested over FPGAs before being considered for ASIC fabrication for mass production. The use of commercial typical cameras (30fps) limits the capabilities of these systems for high speed ap
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Linares-Barranco, A., Rios-Navarro, A., Tapiador-Morales, R., & Delbruck, T. (2019). Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification (ArXiv.Org). https://arxiv.org/abs/1905.07419
