Energy-efficient visual sensing is of paramount importance to enable battery-backed low power IoT and mobile applications. Unfortunately, modern image sensors still consume hundreds of milliwatts of power, mainly due to analog readout. This is because current systems always supply a fixed voltage to the sensor's analog circuitry, leading to higher power profiles. In this work, we propose to aggressively scale the analog voltage supplied to the camera as a means to significantly reduce sensor power consumption. To that end, we characterize the power and fidelity implications of analog voltage scaling on three off-the-shelf image sensors. Our characterization reveals that analog voltage scaling reduces sensor power but also degrades image quality. Furthermore, the degradation in image quality situationally affects the task accuracy of vision applications.

Energy-efficient visual sensing is crucial for enabling battery-backed low power IoT and mobile applications. However, modern image sensors still exhibit high power consumption, primarily attributed to analog readout. This high power consumption arises from the conventional practice of supplying a fixed voltage to the analog circuitry of image sensors. Towards that end, we propose to aggressively scale analog voltage supplied to the camera as means to significantly reduce sensor power consumption. This demonstration showcases the potential of dynamic voltage scaling on commercial image sensors within an RPi-based video streaming pipeline. We enable users to flexibly configure sensor voltage settings and observe the impact on both image fidelity and sensor power consumption. Furthermore, this demonstration offers a framework for studying the implications of flexible voltage specification in the context of a person tracking application.

Energy-efficient visual sensing is extremely important to enable battery powered mobile and IoT applications. While several scheduling techniques have been proposed to save the digital power of sensing, the analog power [1] of capturing an image still remains a daunting barrier for a camera's energy-efficiency. To that end, we characterize the power and performance implications of analog voltage scaling on off-the-shelf image sensors. Our characterization reveals that while reducing the analog voltage supplied to image sensor helps promote sensor power efficiency, it also impairs imaging fidelity, specifically by making images brighter and noisier. Furthermore, we find that brighter and noisier images situationally affect the task accuracy of vision applications. In this poster, we propose an investigation towards a system that adaptively scales analog voltage to optimize sensor energy, while respecting the fidelity needs of visual tasks.

Mimicking physical odor sensations virtually can present users with a real - time odor synthesis that approximates what users would smell in a virtual environment, e.g., as they walk around in virtual reality. To this end, we devise a Smell Engine that includes: (i) a Smell Composer framework that allows developers to configure odor sources in virtual space, (ii) a Smell Mixer that dynamically estimates the odor mix that the user would smell, based on diffusion models and relative odor source distances, and (iii) a Smell Controller that coordinates an olfactometer to physically present an approximation of the odor mix to the user’s mask from a set of odorants channeled through controllable flow valves. Through a three - part user study, we found that the Smell Engine can help measure a subject’s olfactory detection threshold and improve their ability to precisely localize odors in the virtual environment, as compared to existing trigger - based solutions.