TabConv: Low-Computation CNN Inference via Table Lookups

arXiv:2404.05872v1 Announce Type: cross
Abstract: Convolutional Neural Networks (CNNs) have demonstrated remarkable ability throughout the field of computer vision. However, CNN inference requires a large number of arithmetic operations, making them expensive to deploy in hardware. Current approaches alleviate this issue by developing hardware-supported, algorithmic processes to simplify spatial convolution functions. However, these methods still heavily rely on matrix multiplication, leading to significant computational overhead. To bridge the gap between hardware, algorithmic acceleration, and approximate matrix multiplication, we propose TabConv, …

abstract arxiv cnn cnns computation computer computer vision convolution convolutional neural networks cs.cv cs.lg cs.ne current deploy functions hardware however inference issue low making networks neural networks operations processes spatial table them type via vision