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2017/09/15 · A hardware streaming architecture is proposed to accelerate convolution and pooling computations for state-of-the-art deep CNNs.
A hardware streaming architecture is proposed to accelerate convolution and pooling computations for state-of-the-art deep CNNs by maximizing local data ...
A hardware streaming architecture is proposed to accelerate convolution and pooling computations for state-of-the-art deep CNNs. It is optimized for energy ...
In this context, this paper describes a novel architecture based on Layer Operation Chaining (LOC) which uses fewer convolvers than convolution layers. A ...
It is optimized for energy efficiency by maximizing local data reuse to reduce off-chip DRAM data access. In addition, image and feature decomposition ...
A hardware streaming architecture is proposed to accelerate convolution and pooling computations for state-of-the-art deep CNNs. It is optimized for energy ...
Abstract Convolutional Neuronal Networks (CNN) implementation on embedded devices is restricted due to the number of layers of some CNN models.
Yuan Du ET AL: "A Streaming Accelerator for Deep Convolutional Neural. Networks with Image and Feature Decomposition for Resource-limited. System Applications ...
2013. A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications. Y Du, L Du ...
2019/11/15 · In this context, this paper describes a novel architecture based on Layer Operation Chaining (LOC) which uses fewer convolvers than convolution ...