Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
Actigraphy, or the non-invasive study of human activity-rest cycles, is a field of study of growing importance as ambulatory and at-home monitoring of patients becomes more popular.
During post-silicon validation and debug, manufactured integrated circuits (ICs) are tested in actual system environments to detect and fix design flaws (bugs). Existing techniques are costly due to ad hoc, manual methods.