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Convolutional neural networks (CNNs) have proven to be promising in various applications such as audio recognition, image classification, and video understanding. Winograd algorithm helps to reduce the complexity of computation in a convolution but suffers from poor compatibility for different convolution shapes. This work introduces a dynamic dimension-level fusion architecture based on Winograd for accelerating different dimensions of CNNs. We explore this Winograd architecture by designing Dimension Fusion, a dimension-level processing engine that dynamically fuses to match the convolution shape of individual CNN layers. The proposed architecture is the first work based on Winograd algorithm to be compatible with all convolution shapes (dimension, stride, and filtersize) and achieves highest PE efficiency up to 1.55x and energy efficiency up to 3.3x compared with the state-of-art accelerators.

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