Abstract

Machine learning (ML) algorithms automate the data-label mapping and decision-making processes. The output of the ML algorithms determines the next step in real-world applications. It is desirable to have ML-based systems that are simultaneously accurate, secure, energy-efficient, low-cost, silently operable, maintainable, customizable, low-delay, and scalable. However, there are tradeoffs among these design goals that impact overall system complexity. In this talk, we will be focusing on the accuracy, security, and energy-efficiency objectives.

We first target the accuracy objective with a new dual-space classification approach: SECRET. While traditional supervised learning approaches operate in the feature space only, SECRET utilizes both feature and semantic spaces in the classification process. It incorporates class affinity and dissimilarity information into the decision process using the semantic space. This property enables SECRET to make informed decisions on class labels, thus enhancing its overall classification performance.

Next, we introduce a simultaneously smart, secure, and energy-efficient Internet-of-Things (IoT) sensor architecture: SSE. In SSE, we use inference in the compressively-sensed domain and transmit data to the base station when an event of interest occurs. On-sensor compression and inference drastically reduce the amount of data that need to be transmitted. A small part of this energy bonus is used to carry out encryption and hashing to ensure data confidentiality and integrity. Overall, we achieve smartness through decision-making inferences, security through encryption and hashing, and energy efficiency through both compression and decision-making inferences.