NEWS
CSL and ECE Associate Professor Michael Bailey and Adam Bates, an assistant professor in computer science, have helped identify homophones and mistakes in voice processing that could be used to phish Amazon Alexa users.
NASA has tapped CSL professor Farzad Kamalabadi to help with efforts to develop smaller spacecraft at a lower cost.
University of Illinois researchers are working to remove farmer reliance on chemicals to destroy weeds through the use of robots.
Events
CSL 2018 PhD Thesis Award Presentation: Mingu Kang
Selected annually, the CSL PhD Thesis Award is a prestigous honor bestowed to one graduate annually. This year's winner, Mingu Kang, will return to campus to accept the award from the committe and his adviser Naresh Shanbhag. Kang will also give a presentation of his award winning research, a summary of which can be found below.
There is much interest in embedding data analytics and artificial intelligence capabilities into sensor-rich platforms such as wearables, biomedical devices, autonomous vehicles, robots, and the Internet of Things (IoT) to provide them with decision-making capabilities. Such platforms need to implement machine learning algorithms under severe resource constraints on embedded battery-powered platforms. However, the traditional digital von Neumann architecture suffers from the “Memory Wall,” i.e., the interface separating memory and computation. The Wall dominates both the energy efficiency and the throughput in digital architectures, especially in those that implement machine learning systems. Deep in-memory architectures (DIMA) overcome the Wall by having analog computations embedded in close proximity to a standard memory array (before sense amplification). DIMA employs low-signal-to-noise-ratio (low-SNR) analog processing and delayed decisions to achieve both energy efficiency and throughput gains without any loss in inference accuracy relative to a conventional digital architecture. The DIMA concept has been validated via three silicon IC prototypes in 65 nm CMOS, demonstrating up to 100X gains in the energy-delay product for decision-making systems. Furthermore, a DIMA-based in-memory instruction set architecture with an LLVM-based compiler to provide an end-to-end application-mapping software framework has also been developed, demonstrating DIMA’s versatility. This talk will describe the DIMA concept, design principles, challenges, silicon-measured results, and future prospects.
CSL 2018 PhD Thesis Award Presentation: Mingu Kang
Selected annually, the CSL PhD Thesis Award is a prestigous honor bestowed to one graduate annually. This year's winner, Mingu Kang, will return to campus to accept the award from the committe and his adviser Naresh Shanbhag. Kang will also give a presentation of his award winning research, a summary of which can be found below.
There is much interest in embedding data analytics and artificial intelligence capabilities into sensor-rich platforms such as wearables, biomedical devices, autonomous vehicles, robots, and the Internet of Things (IoT) to provide them with decision-making capabilities. Such platforms need to implement machine learning algorithms under severe resource constraints on embedded battery-powered platforms. However, the traditional digital von Neumann architecture suffers from the “Memory Wall,” i.e., the interface separating memory and computation. The Wall dominates both the energy efficiency and the throughput in digital architectures, especially in those that implement machine learning systems. Deep in-memory architectures (DIMA) overcome the Wall by having analog computations embedded in close proximity to a standard memory array (before sense amplification). DIMA employs low-signal-to-noise-ratio (low-SNR) analog processing and delayed decisions to achieve both energy efficiency and throughput gains without any loss in inference accuracy relative to a conventional digital architecture. The DIMA concept has been validated via three silicon IC prototypes in 65 nm CMOS, demonstrating up to 100X gains in the energy-delay product for decision-making systems. Furthermore, a DIMA-based in-memory instruction set architecture with an LLVM-based compiler to provide an end-to-end application-mapping software framework has also been developed, demonstrating DIMA’s versatility. This talk will describe the DIMA concept, design principles, challenges, silicon-measured results, and future prospects.
56th Annual Allerton Conference on Communication, Control, and Computing
October 2-5, 2018 University of Illinois
Each year, the Allerton Conference draws some of the brightest minds from industry, academia and government to discuss innovation in the fields of communication, control and computing. We invite you to submit your work and join us at Allerton Park and Retreat Center for an exciting few days of presentations, special events and networking. 2018 Conference Co-Chairs: Negar Kiyavash and Daniel Liberzon
CSL Social Hour
Fridays at 3pm, CSL 369 - University of Illinois
Join us for CSL Social Hour each Friday at 3 p.m. during the academic year. It's a great opportunity to network with fellow researchers and learn more about the breadth of work underway in CSL. Refreshments will be served. The event is hosted in CSL 369.
All Events
About CSL
The University of Illinois’ Coordinated Science Laboratory is a premier, multidisciplinary research laboratory that focuses on information technology at the crossroads of circuits, computing, control, and communications.
Led by a faculty of world-renowned experts and researchers, CSL uses these innovations to explore critical issues in defense, medicine, environmental sciences, robotics, life-enhancement for the disabled, and aeronautics.
