if youve ever used a smartphone or a fitness tracker youve likely been using a device thats a part of the internet of things a group of interconnected devices that communicate via remote servers but these central servers process huge amounts of data coming in from multiple devices that in turn cause processing delays diminishing user experience to boost data processing speeds dr zhangyang (atlas) wang assistant professor in the department of computer science and engineering has been awarded two grants from the national science foundation (nsf) for developing highly efficient and energy-saving machine learning algorithms for use on local devices rather than on remote servers wed like to develop algorithms that make devices like your phone run faster have low memory use and be more energy efficient wang says so if you want to use your iphone to take a photo with a facial recognition software for example energy efficient algorithms like the ones we develop are what ensure your battery doesnt drain within five minutes wangs first project enabling intelligent cameras in internet-of-things via a holistic platform algorithm and hardware co-design was awarded by the nsf energy power control and networks program and is a collaboration with yingyan lin and richard baraniuk from rice university this project will improve machine learning algorithms for object recognition and other computer vision applications such as traffic monitoring and self-driving cars particularly wangs team will optimize a type of machine learning algorithm called deep neural networks to process images for the second project harmonizing predictive algorithms and mixed-signal/precision circuits via computation-data access exchange and adaptive dataflows wangs research team has been awarded a total of $138 million from the nsf real-time machine learning (rtml) program with collaborators lin and baraniuk from rice university boris murmann from stanford university and yiran chen from duke university wang aims to enhance machine learning programs by training these algorithms in real time for a particular task like object recognition this approach will make the algorithms progressively better at doing the task over time one of the many applications of his research is drone-based object detection systems that can learn new environments without human supervision if we think of monitoring wide terrains for rescue operations drones using our algorithms will be able to survey a large area more efficiently while continuously learning the layout of the new environment wang says when their algorithms are ready for public use wang and his collaborators plan to post them on openstax a nonprofit online education company created by baraniuk so as to lower the cost for people to access educational resources and encourage a global effort to address challenges in developing and improving software related to computer vision the nsf and defense advanced research projects agency (darpa) have teamed up to issue these grants through the rtml crosscutting program to explore high-performance energy-efficient hardware and machine learning architectures that can learn from a continuous stream of new data in real time the rtml program is a part of darpas electronics resurgence initiative a five-year $15 billion investment in the future of domestic us government and defense electronics systems out of over 100 submissions wangs team was one of only six selected for large grants further solidifying his groups leading role in the machine learning field