A Hardware Platform for Efficient Multi-Modal Sensing with Adaptive Approximation
This addresses the problem of sensor energy optimization for researchers in approximate computing and energy-scavenged systems, representing an incremental hardware design.
The researchers tackled the problem of enabling efficient multi-modal sensing in energy-constrained systems by developing Warp, a miniature hardware platform that integrates 11 sensor ICs and an energy-scavenged power supply, achieving a compact size of 3.6 cm x 3.3 cm x 0.5 cm and demonstrating tradeoffs between performance, energy efficiency, and accuracy.
We present Warp, a hardware platform to support research in approximate computing, sensor energy optimization, and energy-scavenged systems. Warp incorporates 11 state-of-the-art sensor integrated circuits, computation, and an energy-scavenged power supply, all within a miniature system that is just 3.6 cm x 3.3 cm x 0.5 cm. Warp's sensor integrated circuits together contain a total of 21 sensors with a range of precisions and accuracies for measuring eight sensing modalities of acceleration, angular rate, magnetic flux density (compass heading), humidity, atmospheric pressure (elevation), infrared radiation, ambient temperature, and color. Warp uses a combination of analog circuits and digital control to facilitate further tradeoffs between sensor and communication accuracy, energy efficiency, and performance. This article presents the design of Warp and presents an evaluation of our hardware implementation. The results show how Warp's design enables performance and energy efficiency versus ac- curacy tradeoffs.