COMNET : CORTICAL MODULES ARE POWERFUL

Ashish Kumar, Laxmidhar Behera

Keywords: CNN Architecture, Multi-dimensional efficiencies, Cortical Modules, Columnar Structure, Real-Time Applications, Latency

TL;DR: A novel CNN architecture leveraging biological structures in visual cortex to cater real-time applications with low latency, smaller depths

Abstract: Existing CNN architectures may achieve efficiency in either one or two dimensions: FLOPs, depth, accuracy, representation power, latency but not in all. In this work, we present a pragmatically designed novel CNN architecture “CoMNet” which offers multi-dimensional efficiency at once such as: simple yet accurate, lower latency and FLOPs, high representation power in limited parameters, low memory consumption, negligible branching, smaller depths, and only a few design hyperparameters. The key to achieve the multi-dimensional efficiency is our use of biological underpinnings into CoMNet which is primarily the organization of cortical modules in the visual cortex. To realize CoMNet, a few concepts from well understood CNN designs are directly inherited such as residual learning. Our solid experimental evaluations demonstrate superiority of CoMNet over many state-of-the-art industry and academia dominant architectures such as ResNet, RepVGG etc. For instance, CoMNet supersedes ResNet-50 on ImageNet while being 50% shallower, 22% lesser parameters, 25% lower FLOPs and latency, and in 16% lesser training epochs. Code will be opensourced post the reviews.

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