Cyber-physical systems rely on continuous sensing of real-world phenomena to guide data collection, storage, analysis, computation, communication, decision-making, and actuation. The sensors and the sensory processing system are often deployed in highly energy-constrained environments, where the power consumed to analyze the raw data stream dominates the energy profile of the entire platform. Typically, the data stream must be analyzed for temporal and spatial trigger signatures that either identify that an event of interest has occurred or, conversely, rule out the possibility of such an event. Trigger signatures can be detected flexibly in software by analyzing the sensory stream as it arrives, but this requires a significant energy budget. This project plans to develop a low-power reconfigurable hardware/software substrate that is tailored for 1) identifying temporal and spatial trigger signatures in the input data streams and 2) dynamically invoking software routines for further processing and on-the-fly reconfiguration. The hardware substrate may be suitable for processing multiple input modalities and time scales, will be easy to train and reconfigure for a broad range of trigger signatures, and will consume minimal energy, thereby making it suitable for continuous deployment in practical scenarios.
The work described in this proposal may enable widespread deployment of continuous sensing applications at lower cost and in mobile or remote environments with strict energy constraints. There are many domains impacting society that may be applicable for such systems. Many new and emerging end-user applications in the mobile space also rely on environmental sensing to trigger context- and location- aware computation or communication. For example, mobile phone accelerometer data can be used to infer the type and level of activity of the user, as well as his or her specific location or context (e.g. climbing into the driver's seat of a car generates an identifiable accelerometer signature which can be used to disable text messaging while driving). The ability to flexibly deploy continuous sensing for these and other applications has the potential to change these markets and create entirely new and unforeseen application domains.
This project investigated the technical and commercial viability of a novel coprocessor that enables continuous sensing with ultra-low power consumption. Cyber-physical systems rely on continuous sensing of real-world phenomena to guide data collection, storage, analysis, computation, communication, decision-making, and actuation. The sensors and the first tier of the sensory processing system are often deployed in highly energy-constrained environments, where the power consumed to analyze the raw data stream dominates the energy profile of the entire platform, and can quickly deplete the battery. Typically, the data stream must be analyzed for temporal and spatial trigger signatures that either identify that an event of interest has occurred or, conversely, rule out the possibility of such an event. Trigger signatures can be detected flexibly in software by analyzing the sensory stream as it arrives, but this requires a significant energy budget, particularly for continuous and/or high-volume data streams. In contrast, this project investigated a novel low-power reconfigurable hardware/software substrate that is tailored for 1) identifying temporal and spatial trigger signatures in the input data streams and 2) dynamically invoking software routines for further processing and on-the-fly reconfiguration. The investigated hardware substrate will be suitable for processing multiple input modalities and time scales, will be easy to train and reconfigure for a broad range of trigger signatures, and will consume minimal energy, thereby making it suitable for continuous deployment in practical scenarios. Without dramatic innovations in the development of ultra-low-power sensory processing, continuous sensing will remain a niche application limited to environments with a stable and plentiful power source. The work completed under this award will enable widespread deployment of continuous sensing applications at lower cost and in mobile or remote environments with strict energy constraints. Example domains for such systems include medical, health, and safety monitoring of critical sensory data streams such as human EKG, EEG, pulse, blood pressure, and patient activity levels, or environmental monitoring of emission levels, pollutant concentrations, or even seismic data. Many new and emerging end-user applications in the mobile space also rely on environmental sensing to trigger context- and location-aware computation or communication. The ability to flexibly deploy continuous sensing for these and other applications has the potential to revolutionize these markets and create entirely new and unforeseen application domains.
