Batteryless, energy-harvesting sensing systems are critical to the Internet-of-Things (IoT)
vision and sustainable, long-lived, untethered systems. Unfortunately, developing new …

This paper presents a sample-based energy simulation methodology that enables fast and
accurate estimations of performance and average power for arbitrary RTL designs. Our …

Implementing a neural network (NN) inference in a millimeter-scale system is challenging
due to limited energy and storage size. This article proposes an energy-aware adaptive NN …

This paper describes a miniature edge device that performs neural network inference with
different exit options depending on available energy. In addition to the main-exit path, it …

Each fall, millions of monarch butterflies across the northern US and Canada migrate up to
4,000 km to overwinter in the exact same cluster of mountain peaks in central Mexico. To …

The Internet of Things (IoT) is a rapidly evolving application space. One of the fascinating
new fields in IoT research is mm-scale sensors, which make up the Internet of Tiny Things …

The interconnection of digital and physical domains is growing fast as a result of the
enormous activity in a number of application and research fields, such as the Internet of …

Millimeter-scale embedded sensing systems have unique advantages over larger devices
as they are able to capture, analyze, store, and transmit data at the source while being …

I/O has become the limiting factor in scaling down size and power toward the goal of
invisible computing. Achieving this goal will require composing optimized and specialized …

This brief presents an energy-efficient accelerator for convolutional neural network (CNN)
layer computations in a compact system. The accelerator replaces traditional data shift …