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Design Space Exploration of FPGA-Based Deep Convolutional Neural Networks

Abstract Deep Convolutional Neural Networks (DCNN) have proven to be very effective in many pattern recognition applications, such as image classification and speech recognition. Due to their computational complexity, DCNNs demand implementations that utilize custom hardware accelerators to meet performance and energy-efficiency constraints. In this paper, we propose an FPGA-based accelerator architecture which leverages all sources of parallelism in DCNNs. We develop analytical feasibility and...
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