This paper proposes a composite inner-product computation unit based on left-to-right (LR) arithmetic for the acceleration of convolution neural networks (CNN) on hardware. The efficacy of the proposed L2R-CIPU method has been shown on the VGG-16 network, and assessment is done on various performance metrics. The L2R-CIPU design achieves 1.06x to 6.22x greater performance, 4.8x to 15x more TOPS/W, and 4.51x to 53.45x higher TOPS/mm2 than prior architectures.
@article{arxiv.2406.00360,
title = {L2R-CIPU: Efficient CNN Computation with Left-to-Right Composite Inner Product Units},
author = {Malik Zohaib Nisar and Mohammad Sohail Ibrahim and Muhammad Usman and Jeong-A Lee},
journal= {arXiv preprint arXiv:2406.00360},
year = {2024}
}