{'fraction_non_alphanumeric': 0.019438444924406047, 'fraction_numerical': 0.0, 'mean_word_length': 5.3061224489795915, '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}}
Adaptive Camera Platform Using Deep Learning-Based Early Warning of Task Failures

There is a growing interest in deploying complex deep neural networks (DNN) in smart sensors to extract task-specific information from real-time sensor data. This paper presents an adaptive sensor that uses a novel light-weight deep learning platform, WarningNet, to estimate potential task failures due to perturbations in the sensor data and control its read-out-circuit (ROIC) parameters such as sampling resolution and operating voltage. Simulation results show that WarningNet can provide early warning of the performance degradation of various tasks within a fraction of the time required for the task to complete. Moreover, the early warning guided adaptive control of sampling resolution and operating voltage of the ROIC of a digital pixel sensor shows potential of dynamic trade off between task accuracy and sensor energy/bandwidth.