{'fraction_non_alphanumeric': 0.024158757549611734, 'fraction_numerical': 0.0025884383088869713, 'mean_word_length': 5.666666666666667, '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}}
Memory Optimization for Energy-Efficient Differentially Private Deep Learning

With the advent of Internet of Things (IoT) technologies and availability of a large amount of data, deep learning has been applied in a variety of artificial intelligence (AI) applications. However, sharing personal data using IoT edge devices carries inherent risks to individual privacy. Meanwhile, the energy and memory resources needed during the inference process become a constraint to the resource-limited IoT edge devices. This article brings memory hardware optimization to meet the tight power budget in IoT edge devices by considering the privacy, accuracy, and power efficiency tradeoff in differentially efficient deep learning systems. Based on a detailed analysis on these characteristics, an integer linear programs (ILP) model is developed to minimize mean square error (MSE), thereby enabling optimal input data memory design. Our simulation results in 45-nm CMOS technology show that the proposed technique can enable near-threshold energy-efficient memory operation for different privacy requirements, with less than 1% degradation in classification accuracy.