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On June 6th, 2019, C3SR members were invited to present the center’s work on MLModelScope at the MLPerf community’s meeting in Santa Clara, CA. At the meeting, Dr. Jinjun Xiong on behalf of the team presented “Benchmarking and Understanding ML Inference,” a talk drawing on research results from MLModelScope. Among the MLModelScope’s team members, Ms. Cheng Li also attended the meeting in person, while Mr. Abdul Dakkak attended the meeting online.

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For the second year in a row, our C3SR teams are victorious in a competition challenging experts worldwide to design low-power embedded systems for Internet-of-Things (IoT) applications. At the 2019 Design Automation Conference (DAC) System Design Contest, our teams won Double Championships for both the GPU and the FPGA tracks of the contest. The contest’s objective is to create algorithms that can accurately detect and locate objects from images taken by aerial drones.

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C3SR faculty, Prof. Lav Varshney, was invited to give a talk at the opening ceremony of the 2019 National Science Olympiad competition. There were more than 2,000 top middle and high school students from around the country.

Here is the news story.

Prof. Varshney’s talk was well received by the thousands of enthusiastic students.

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On April 17, 2019, C3SR members Ms. Cheng Li, Mr. Abdul Dakkak, Prof. Jinjun Xiong and Prof. Wen-mei Hwu were invited to give a tutorial on the center’s work on MLModelScope at the 2019 ASPLOS conference in Providence, RI.

The tutorial was well received by the attendees. Details about this tutorial can be found here.

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One of the joint research work between the C3SR team and the IBM Research, titled “Evaluating Characteristics of CUDA Communication Primitives on High-Bandwidth Interconnects,” won the Best Research Paper Award at the 10th ACM/SPEC International Conference on Performance Engineering (ICPE).

The winning team members were Carl Pearson, Abdul Dakkak, Sarah Hashash, Cheng Li, I-Hsin Chung, Jinjun Xiong, and Wen-mei Hwu.

Congratulations!

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On Jan 29th, 2019, C3SR members held one of UIUC Engineering Research Fair booths in the university union for the undergraduate students. This event included poster sessions where we presented our works such as DNNBuilder and Dr. Babel fish.

You can find more details of the C3SR undergraduate research opportunities via
the ISUR C3SR-URAI program.

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The C3SR Center is partnering with Illinois Scholars Undergraduate Research (ISUR) Program to launch a new innovative program, titled Undergraduate Research in Artificial Intelligence (URAI). The new initiative, ISUR C3SR-URAI, will provide two-semester research experiences to domestic and international students. C3SR-URAI scholars will be assigned to research projects focusing on fundamental cognitive computing and AI science, applied AI engineering and technologies, and practical AI solutions and systems. They will receive the same benefits as any ISUR scholar, such as $1000 research scholarship for the academic year ($500/semester), participation in research seminar series luncheons, and presentation of research projects in the year-end ISUR Poster Expo.

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On Nov 16, 2018, IBM VP, Ms. Radha Ratnaparkhi, and Director, Dr. Lisa Amini, as representatives of the IBM AIHN program came to the C3SR center and conducted the year-end C3SR Executive Review.

You can find more details about the IBM AI Horizons Network here.

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One of the joint research work between the C3SR team and the IBM China Research Lab, titled “DNNBuilder: an Automated Tool for Building High-Performance DNN Hardware Accelerators for FPGAs,” won IEEE/ACM William J. McCalla Best Paper Award at the 2018 International Conference on Computer-Aided Design (ICCAD).

The winning team members were Xiaofan Zhang, Junsong Wang, Chao Zhu, Yonghua Lin, Jinjun Xiong, Wen-mei Hwu and Deming Chen.

Congratulations!

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On Nov 1, 2018, two of IBM’s Fellows, Dr. Steve Fields and Dr. Hillery Hunter visited C3SR and reviewed the Center’s AI Systems work, in particular, the Erudite system where we make storage-class memory via FLASH possible and Acceleration of AI Workloads via FPGA in the Cloud and at the Edges.

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