Because embedded sensor platforms can dissipate most of their energy in accessing sensor integrated circuits such as gyroscopes, it is important and interesting to develop techniques to reduce the power dissipated when sensors are active.


Lax is a device driver abstraction for interacting with sensors that enables power savings in exchange for occasionally returning erroneous sensor data.

Our implementation on a hardware prototype delivers savings in sensor dynamic power dissipation of up to 48 percent (as compared to precise device access) while providing sensor access error rates lower than 5 data acquisition errors per 100 data accesses. Given the significant proportion of system energy budgets in wearable platforms that are devoted to sensors, approximate sensor data acquisition using Lax can deliver significant system-level energy savings.


Status: This is an ongoing project. If you are interested in collaborating on some of the unsolved problems or applications, please get in touch!

Collaborations: This is joint work with Martin Rinard (MIT)



  1. P. Stanley-Marbell and M. Rinard. "Lax: Driver Interfaces for Approximate Sensor Device Access". USENIX HotOS XV, 2015.

  2. P. Stanley-Marbell and M. Rinard. "Warp: A Hardware Platform for Efficient Multi-Modal Sensing with Adaptive Approximation".  Accepted for publication, IEEE Micro, 2018.

    The accuracy and precision of the data from sensors varies with their electrical parameters such as supply voltage and with configuration parameters such as digital sensor configuration register settings. These in turn dictate the energy efficiency of sensors. Because many sensor signal processing applications are designed to cope with the noisy physical world, they can often tolerate small amounts of additional sensor noise and to do so if it enables lower energy usage. This article presents the concept, design, and evaluation a first-of-its-kind research instrument with 22 sensors integrated into a miniature energy-scavenged system that enables fundamentally new research in sensor noise versus energy efficiency tradeoffs.