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This paper introduces a novel network topology that seamlessly integratesdynamic inference cost with a top-down attention mechanism, addressing twosignificant gaps in traditional deep learning models. Drawing inspiration fromhuman perception, we combine sequential processing of generic low-levelfeatures with parallelism and nesting of high-level features. This design notonly reflects a finding from recent neuroscience research regarding - spatiallyand contextually distinct neural activations - in human cortex, but alsointroduces a novel "cutout" technique: the ability to selectively activate%segments of the network for task-relevant only network segments oftask-relevant categories to optimize inference cost and eliminate the need forre-training. We believe this paves the way for future network designs that arelightweight and adaptable, making them suitable for a wide range ofapplications, from compact edge devices to large-scale clouds. Our proposedtopology also comes with a built-in top-down attention mechanism, which allowsprocessing to be directly influenced by either enhancing or inhibitingcategory-specific high-level features, drawing parallels to the selectiveattention mechanism observed in human cognition. Using targeted externalsignals, we experimentally enhanced predictions across all tested models. Interms of dynamic inference cost our methodology can achieve an exclusion of upto $73.48\,\%$ of parameters and $84.41\,\%$ fewer giga-multiply-accumulate(GMAC) operations, analysis against comparative baselines show an averagereduction of $40\,\%$ in parameters and $8\,\%$ in GMACs across the cases weevaluated.

High-Level Parallelism and Nested Features for Dynamic Inference Cost and Top-Down Attention