2022
DOI: 10.1109/access.2022.3192618
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On-the-Fly Lowering Engine: Offloading Data Layout Conversion for Convolutional Neural Networks

Abstract: Many deep learning frameworks utilize GEneral Matrix Multiplication (GEMM)-based convolution to accelerate CNN execution. GEMM-based convolution provides faster convolution yet requires a data conversion process called lowering (i.e., im2col), which incurs significant memory overhead and diminishes performance. This paper proposes a novel hardware mechanism, called On-the-fly Lowering Engine (OLE), to eliminate the lowering overheads. Our goal is to offload the lowering overheads from the GEMMbased convolution… Show more

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