Efficient Computing for Machine Learning at the Edge
Beyond Approximate Computing: Enabling Full-System Energy-Quality Scalability in Embedded Systems
National Science Foundation
NSF AI Institute for Edge Computing Leveraging Next Generation Networks
National Science Foundation
Enabling a Global Spectrum Observatory through Mobile, Wide-band Spectrum Sensing Kits and a Software Ecosystem
National Science Foundation

SynthNet (ASP-DAC 2022)
A high-throughput yet energy-efficient combinational logic neural network

SECO (ISLPED 2019)
A scalable accuracy approximate exponential function via cross-level optimization

SAADI (ASP-DAC 2019)
A scalable accuracy approximate divider for dynamic energy-quality scaling
Usable Security and Privacy for Mobile and Embedded Systems
Sponsors
National Science Foundation, Intel Corporation, Meta (Facebook)

Kaleido (USENIX Security 2021)
Real-time privacy control for eye-tracking systems

VoltKey (UbiComp 2019)
Continuous secret key generation based on power line noise for zero-involvement pairing and authentication

CamPUF (DAC 2018)
Physically unclonable function based on CMOS image sensor fixed pattern noise
Embedded Systems Applications
Mitigating Heat Stress in Dairy Cattle using a Physiological Sensing-Behavior Analysis-Microclimate Control Loop
USDA National Institute of Food and Agriculture

CompAg 2020
Using implantable biosensors and wearable scanners to monitor dairy cattle’s core body temperature in real-time

TMSCS 2019
Design and management of battery-supercapacitor hybrid electrical energy storage systems for regulation services

TeleProbe (ISLPED 2016)
Zero-power contactless probing for implantable medical devices
Research Sponsors
We appreciate the generous sponsors who make our research possible