Energy-Efficient and Reliable Sensor Platform Based on Analog-to-Feature Extraction

This paper introduces the Artificial Intelligence-Driven Energy-efficient Analog-to-Learning system (AIDEAL), an AI-enabled in-sensor computing platform designed to enhance energy efficiency in edge devices through in situ data processing. AIDEAL extracts task-relevant features from analog inputs, significantly reducing the data volume for digitization and transmission, by utilizing analog compute-in-memory (ACIM). AIDEAL demonstrates substantial benefits across various applications including image reconstruction, image classification, and object detection. Notably, it achieves 60.2%-80.2% reduction in sensor energy for image classification compared to traditional methods of digitizing and transmitting full input data. We also examine AIDEAL under different feature reduction rates and variations in voltage, and temperature in ACIM. Furthermore, we introduce two algorithmic strategies, feature restoration (FR) and hamming weight based energy aware quantization (HEQA), which enhance accuracy and energy efficiency. These methods result in less than 2% accuracy drop and 37% energy savings compared to baseline, respectively.