Learn about how researchers combine ARA, an at-scale platform for advanced wireless research and CHI@Edge as a baseline software platform for agriculture applications including high-throughput crop phenotyping, precision livestock farming, agriculture automation and rural education.
Learn about IndySCC, a virtual version of SuperComputing's Student Cluster Competition (SCC) which was conducted on Chameleon! Learn all about the competition, how it was run with Chameleon, and of course, the winners!
Learn all about extending Pegasus, a fully featured workflow management system, to edge devices in one of the first examples of research done on CHI@Edge, Chameleon's edge computing testbed.
Researchers at the University of Missouri explore novel middle-man data poisoning attacks with facial authentication applications and propose a defense architecture. Learn more about these attacks, their solution, and the researchers themselves in November's featured user experiments blog.
Learn about University of Chicago PhD student Chengcheng Wan's research on designing a solution to integrate machine learning components into traditional software systems to maximize energy efficiency, latency, and accuracy constraints at machine learning, system and application levels, with related research published at USENIX ATC, ICML, and ICSE.
University of Texas, San Antonio Professor Wei Wang and PhD student Sen He investigate performance testing for cloud computing research to help make your research more efficient and cost effective. Learn about their research, which won the ACM SIGSOFT Distinguished Paper award in 2019, experience on Chameleon and AWS, and life philosophies.
This blog features Stevens Institute of Technology PhD candidate Batyr Charyyev’s research on using network traffic fingerprinting of IoT devices for device identification, anomaly detection and user interaction identification. Learn more about Charyyev and his research, including its applications to infer voice commands to smart home speakers.
Interested in learning how you can save power in computer systems, especially with latency critical applications? Learn more about how profile-guided frequency scaling can help solve this problem, with research supervised by Assistant Professor Vinicius Petrucci at the University of Pittsburgh and presented last month at IEEE/ACM CCGrid!
This work is part of George Mason University PhD student Zheng Chai and Prof. Yue Cheng’s research on solving federated learning (FL) bottlenecks for edge devices. Learn more about the authors, their research, and their novel FL training system, FedAT which already has impressive results, improving prediction performance by up to 21.09% and reducing communication cost by up to 8.5 times compared to state-of-the-art FL systems.
This blog feature explores 4th year University of Michigan PhD student Peifeng Yu’s research on hyperparameter tuning, presented earlier this month at MLSys21. Learn more about Yu, the hyperparameter tuning engine, and how it can improve your deep learning model training process.