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CALL FOR CHALLENGES

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2019 BenchCouncil International AI System and Algorithm Challenges

http://www.benchcouncil.org/competition/index.html

Awards: 500,000 CNY (about 70,000 US dollar)

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Introduction ------------------------

BenchCouncil 2019 International AI system and algorithm challenges are organized by International Open Benchmark Council (BenchCouncil), and the main purpose is to advance the state-of-the-art and state-of-the-practice algorithms on different systems or architectures, i.e., RISC-V, Cambrian chip, and X86_64, and solicit new approaches to advance the state-of-the-art or state-of-the-practice algorithms. The challenge tracks use AIBench as baseline, which is publicly available from http://www.benchcouncil.org/AIBench/index.html . BenchCouncil provides the tesbed for reproducing performance numbers. The competition team can apply for nodes through http://www.benchcouncil.org/testbed/index.php .

2019 AI challenges have four tracks: • International AI System Challenge based on RISC-V • International AI System Challenge based on Cambrian Chip • International AI System Challenge based on X86 Platform • International 3D Face Recognition Algorithm Challenge

Challenges manuals ------------------------ http://www.benchcouncil.org/competition/handbook-en.pdf

Communication Tool ------------------------

The discussion groups are hosted on Xinxiu---a dedicated communication tool for science and education. Discussion group site: https://app.ic3i.com/org/benchcouncil/en/

Important Dates ------------------------ Now the AI challenge is beginning! Registration deadline: Sep. 15, 2019, anywhere on earth Performance numbers finalized: October 1, 2019, anywhere on earth The code should be submitted to BenchHub for reproducing performance numbers and code review. Submission site: http://125.39.136.212:8090 Preliminary paper version submitted: October 15, 2019, anywhere on earth Paper submission： https://easychair.org/conferences/?conf=competition2019 Camera-ready version submitted: November 10, 2019

Awards ------------------------ Special Award (Only one): 100,000 CNY The First Prize: • 30,000 CNY (one for every track) The Second Prize: • 20,000 CNY (two for every track) The Third Prize: • 10,000 CNY (three for every track)

Awards Presentation ------------------------ The award presentation is on Bench 19 conference ( http://www.benchcouncil.org/bench19/index.html ), which will be held on Nov 14-16 at Denver, Colorado, USA.

Every team should submit their paper to BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench 19). The award-winners must submit a paper to Bench 19 conference and give a presentation. Bench19 Submission Site: https://easychair.org/conferences/?conf=competition2019

Award committees • Lizy John (University of Texas at Austin) • D. K. Panda (OSU) • Geoffrey Fox (Indianan University) • Wanling Gao (ICT, Chinese Academy of Sciences) • XIaoyi Lu (OSU) • Jianfeng Zhan (ICT, Chinese Academy of Sciences)

AI Challenge Tracks ------------------------ (1) International AI System Competition based on RISC-V Goal • The implementation and optimization of CNN-based image classification task on RISC-V, using Cifar-10 dataset and ResNet-50 model Targets • Implement the forward calculation stage • Minimize external dependences (e.g., OpenMP, Boost) • Guarantee the original model accuracy (deviation<0.05%) Metrics • Maximize the execution performance (number of instructions) • Minimize the binary file, e.g., compiled executable file

(2) International AI System Competition based on Cambrian Chip Goal • The implementation and optimization of CNN-based image classification task on Cambrian, using Cifar-10 dataset and ResNet-50 model Target • Implement the forward calculation stage • Guarantee the original model accuracy (deviation<0.05%) Metric • Maximize the execution performance---the shorter the prediction time on test data, the better the performance

(3) International AI System Competition based on X86 Platform Goal • The implementation and optimization of matrix decomposition based collaborative filtering task on X86 platform, using MovieLens dataset and ALS-WR algorithm Target • Implement ALS-WR training algorithm • Can use external libraries supported by the platform Metric • Maximize the execution performance-reduce training time (30 rounds)

(4) International 3D Face Recognition Algorithm Competition Goal • Innovative algorithm for 3D Face Recognition Targets • The competitors need to submit the model file and test file • Description file, source code • External data for training is allowed, but need description Metrics • ROC and AUC value