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With the increasing integration of machine learning into IoT devices, managing energy consumption and data transmission has become a critical challenge. Many IoT applications depend on complex computations performed on server-side infrastructure, necessitating efficient methods to reduce unnecessary data transmission. One promising solution involves deploying compact machine learning models near sensors, enabling intelligent identification and transmission of only relevant data frames. However, existing near-sensor models lack adaptability, as they require extensive pre-training and are often rigidly configured prior to deployment. This paper proposes a novel framework that fuses online learning, active learning, and knowledge distillation to enable adaptive, resource-efficient near-sensor intelligence. Our approach allows near-sensor models to dynamically fine-tune their parameters post-deployment using online learning, eliminating the need for extensive pre-labeling and training. Through a sequential training and execution process, the framework achieves continuous adaptability without prior knowledge of the deployment environment. To enhance performance while preserving model efficiency, we integrate knowledge distillation, enabling the transfer of critical insights from a larger teacher model to a compact student model. Additionally, active learning reduces the required training data while maintaining competitive performance. We validated our framework on both benchmark data from the MS COCO dataset and in a simulated IoT environment. The results demonstrate significant improvements in energy efficiency and data transmission optimization, highlighting the practical applicability of our method in real-world IoT scenarios. 

Introducing HyperSense, the co‐designed hardware and software system efficiently controls analog‐to‐digital converter (ADC) modules’ data generation rate based on object presence predictions in sensor data. Addressing challenges posed by escalating sensor quantities and data rates, HyperSense reduces redundant digital data using energy‐efficient low‐precision ADC, diminishing machine learning system costs. Leveraging neurally inspired hyperdimensional computing, HyperSense analyzes real‐time raw low‐precision sensor data, offering advantages in handling noise, memory‐centricity, and real‐time learning. The proposed HyperSense model combines high‐performance software for object detection with real‐time hardware prediction, introducing the novel concept of intelligent sensor control. Comprehensive software and hardware evaluations demonstrate the solution's superior performance, evidenced by the highest area under the curve and sharpest receiver operating characteristic curve among lightweight models. Hardware‐wise, the field programmable gate array‐based domain‐specific accelerator tailored for HyperSense achieves a 5.6× speedup compared to YOLOv4 on NVIDIA Jetson Orin while showing up to 92.1% energy saving compared to the conventional system. These results underscore HyperSense's effectiveness and efficiency, positioning it as a promising solution for intelligent sensing and real‐time data processing across diverse applications.