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Deep neural networks (DNNs) have attracted significant attention for their excellent accuracy especially in areas such as computer vision and artificial intelligence. To enhance their performance, technologies for their hardware acceleration are being studied. FPGA technology is a promising choice for hardware acceleration, given its low power consumption and high flexibility which makes it suitable...
CPU-FPGA heterogeneous platforms offer a promising solution for high-performance and energy-efficient computing systems by providing specialized accelerators with post-silicon reconfigurability. To unleash the power of FPGA, however, the programmability gap has to be filled so that applications specified in high-level programming languages can be efficiently mapped and scheduled on FPGA. The above...
Artificial Neural Network (ANN) is widely used in machine learning and artificial intelligence areas. But ANN requires a long running time and induces a high power consumption when running on a GPU or CPU which may hinder its application in embedded system. This paper proposes a hardware accelerator design guideline for ANN with arbitrary scales and depths. We take full consideration of the hardware...
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