ACM Student Research Competition at ICCAD 2021 (SRC@ICCAD’21)
DEADLINE: September 28, 2021 (Extended)
Online Submission: https://easychair.org/my/conference?conf=srciccad2021
Sponsored by Microsoft Research, the ACM Student Research Competition is an internationally recognized venue enabling undergraduate and graduate students who are ACM members to:
- Experience the research world — for many undergraduates, this is a first!
- Share research results and exchange ideas with other students, judges, and conference attendees
- Rub shoulders with academic and industry luminaries
- Understand the practical applications of their research
- Perfect their communication skills
- Receive prizes and gain recognition from ACM and the greater computing community.
The ACM Special Interest Group on Design Automation (ACM SIGDA) is organizing such an event in conjunction with the International Conference on Computer Aided Design (ICCAD). Authors of accepted submissions will get ICCAD registration fee support from SIGDA. The event consists of several rounds, as described at http://src.acm.org/, where you can also find more details on student eligibility and timeline.
Details on abstract submission:
Research projects from all areas of design automation are encouraged. The author submitting the abstract must still be a student at the time the abstract is due. Each submission should be made on the EasyChair submission site. Please include the author’s name, affiliation, and email address; research advisor’s name; ACM student member number; category (undergraduate or graduate); research title; and an extended abstract (maximum 2 pages or 800 words) containing the following sections:
- Problem and Motivation: This section should clearly state the problem being addressed and explain the reasons for seeking a solution to this problem.
- Background and Related Work: This section should describe the specialized (but pertinent) background necessary to appreciate the work. Include references to the literature where appropriate, and briefly explain where your work departs from that done by others. Reference lists do not count towards the limit on the length of the abstract.
- Approach and Uniqueness: This section should describe your approach in attacking the problem and should clearly state how your approach is novel.
- Results and Contributions: This section should clearly show how the results of your work contribute to computer science and should explain the significance of those results. Include a separate paragraph (maximum of 100 words) for possible publication in the conference proceedings that serves as a succinct description of the project.
- Single paper summaries (or just cut & paste versions of published papers) are inappropriate for the ACM SRC. Submissions should include at least one year worth of research contributions, but not subsuming an entire doctoral thesis load.
All accepted submissions will be invited to present their work to the community (and a jury) as part of the program for ICCAD 2021 (details on the presentations will follow after acceptance). Note that ICCAD will take place virtually (i.e., as an online event) from November 1 to November 5, 2021.
The ACM Student Research Competition allows both graduate and undergraduate students to discuss their research with student peers, as well as academic and industry researchers, in an informal setting, while enabling them to attend ICCAD and compete with other ACM SRC winners from other computing areas in the ACM Grand Finals.
Online Submission – EasyChair:
- Abstract submission deadline: September 28, 2021 (Extended)
- Acceptance notification: October 12, 2021
- Poster session: November 02, 2021
- Award winners announced at ICCAD
- Grand Finals winners honored at ACM Awards Banquet: June 2022 (Estimated)
Students submitting and presenting their work at SRC@ICCAD’21 are required to be members of both ACM and ACM SIGDA.
Meng Li (Facebook, USA), firstname.lastname@example.org
Cong Hao (Georgia Institute of Technology, USA), email@example.com
Last Year’s Results (2020): SIGDA SRC Gold Medalists won ACM SRC Grand Finals
- Graduate: First Place
Jiaqi Gu, University of Texas at Austin
Research Advisors: David Z. Pan and Ray T. Chen
“Light in Artificial Intelligence: Efficient Neuromorphic Computing with Optical Neural Networks” (ICCAD 2020)
Deep neural networks have received an explosion of interest for their superior performance in various intelligent tasks and high impacts on our lives. The computing capacity is in an arms race with the rapidly escalating model size and data amount for intelligent information processing. Practical application scenarios, e.g., autonomous vehicles, data centers, and edge devices, have strict energy efficiency, latency, and bandwidth constraints, raising a surging need to develop more efficient computing solutions. However, as Moore’s law is winding down, it becomes increasingly challenging for conventional electrical processors to support such massively parallel and energy-hungry artificial intelligence (AI) workloads. .. [Read more]
- Undergraduate: Second Place
Chuangtao Chen, Zhejiang University
Research Advisor: Cheng Zhuo
“Optimally Approximated Floating-Point Multiplier” (ICCAD 2020)
At the edge, IoT devices are designed to consume the minimum resource to achieve the desired accuracy. However, the conventional processors, such as CPU or GPU, can only conduct all the computations with predetermined but sometimes unnecessary precisions, inevitably degrading their energy efficiency. When running data-intensive applications, due to the large range of input operands, most conventional processors heavily rely on floating-point units (FPUs). Recently, approximate computing has become a promising alternative to improve energy efficiency for IoT devices on the edge, especially when running inaccuracy-tolerable applications. For various data-intensive tasks on edge devices, multiplication is a common but the most energy consuming one among different floating-point operations. As a common arithmetic component that has been studied for decades –, the past focus on the FP multiplier is accuracy and performance… [Read more]