Onsensor

Onsensor is a term used in sensor technology to describe the practice of performing data processing on the sensor device itself, rather than delegating all processing to a separate downstream processor. This approach, also called in-sensor processing or on-sensor computing, involves integrating processing elements such as microcontrollers, digital signal processors, FPGAs, or neural processing units on the same package as the sensor or within a surrounding system-on-a-chip designed around the sensor.

The core idea is to enable local computation at the point of data capture. By executing tasks such as pre-processing, feature extraction, compression, or inference directly on the sensor, onsensor aims to reduce data bandwidth, lower latency, and decrease power consumption associated with transmitting raw data. It can also enhance privacy by keeping sensitive information on the device and support real-time decision making in environments with limited connectivity.

Applications span imaging and beyond. In cameras and video surveillance, onsensor processing can enable faster motion detection and reduced bandwidth. Automotive sensors and ADAS systems use in-sensor AI to identify objects in real time. Industrial inspection, robotics, environmental monitoring, and some medical imaging devices also leverage onsensor capabilities to improve responsiveness and efficiency.

Challenges include trade-offs in cost, heat generation, and physical space for processing resources within the sensor package. Firmware and software development become more complex, requiring careful calibration between optical performance and computational load. As sensor technology evolves, industry groups continue to explore standards and ecosystems to improve interoperability between sensors and downstream systems.