{'fraction_non_alphanumeric': 0.01926040061633282, 'fraction_numerical': 0.004622496147919877, 'mean_word_length': 5.730569948186528, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Digital Foveation: An Energy-Aware Machine Vision Framework

In machine vision applications, imaging systems and analysis algorithms are generally interdependent and energy intensive. We describe a machine vision energy minimization framework in which imaging hardware and vision algorithms are co-designed and tightly integrated. Digital foveation is inspired by the human vision system, which uses a spatially varying sensing architecture to generate oculomotory feedback and capture a series of high-resolution images using the densely sampling fovea. A multiround process with bidirectional information flow between camera hardware and analysis software optimizes energy consumption while preserving accuracy. By using existing hardware mechanisms, namely, row / column skipping, random access via readout circuitry, and frame preservation, digital foveation adapts to the chosen analysis algorithm. It aims to transmit and process only the necessary parts of the scene under consideration. This framework is general across a wide range of embedded machine vision applications and enables large improvements in energy efficiency. When evaluated for an embedded license plate recognition vision application, it reduces system energy consumption by 81.3% with at most 0.65% reduction in accuracy.