Embedded sensory data memory optimization for IoT edge inference with privacy, accuracy, and energy efficiency
Jonathon Edstrom, Hritom Das, Yiwen Xu, Na Gong · 2019
In recent years, deep learning is transforming many modern applications. For example, deep learning has demonstrated exceptional performance in disease diagnosis of brain disorders and various forms of cancers due to the availability of a large amount of patients' data. Meanwhile, with the advent of wearable technologies and Internet of Things (IoT), there is a rising interest in providing personalized experience with health recommender systems. Such learning-enabled benefit, however, does come with its own cost, such as associated serious privacy concerns. Sharing personal data carries inherent risks to individual privacy. Due to the substantial requirements for computation and storage resources, today's deep learning systems are typically built upon large, centralized data repositories. Based on this centralized-training paradigm, data owners need to upload their private data to the provider and do not have control over how their private data is being used. To protect privacy, one popular technique is differentially private deep learning algorithms, which add random noise to the computation so that the output does not significantly depend on any particular training sample. When introducing noise, the privacy-guarantee comes at the cost of compromising the inference accuracy of the systems.