Subj: 1st Tournament on Reproducible and Pareto-Efficient Deep Learning at ASPLOS`18

 

Apologies if you receive multiple copies of this call!

 

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CALL FOR CONTRIBUTIONS

 

ReQuEST: 1st Reproducible Quality-Efficient Systems Tournament

Call for Pareto-efficient deep learning

 

Intent to submit: February 5, 2018 AoE

 

Associated ReQuEST workshop co-located with ASPLOS 2018

March 24th, 2018 (afternoon), Williamsburg, VA, USA

 

cKnowledge.org/request

 

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Co-designing emerging workloads across the hardware/software stack to optimize for speed, accuracy, costs and other metrics is extremely complex and time consuming. The lack of a rigorous methodology and common tools for open, reproducible and multi-objective optimization makes it challenging or even impossible to evaluate and compare different published works across numerous heterogeneous hardware platforms, software frameworks, compilers, libraries, algorithms, data sets, and environments.

The 1st ReQuEST workshop aims to bring together multidisciplinary researchers in systems, compilers, architecture and machine learning to optimize the quality vs. efficiency Pareto optimality of deep learning systems on complete hardware/software platforms in a standardized, reproducible and comparable fashion. The target application for the first incarnation of ReQuEST will be the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and will focus solely on optimizing inference on real systems.

Restricting the competition to a single application domain will allow us to test an open-source tournament infrastructure, validate it across multiple platforms and environments, and prepare a dedicated live scoreboard with the “winning” solutions. For future incarnations of ReQuEST, we will provide broader application coverage.

Unlike the classical ILSVRC where submissions are ranked based on accuracy, ReQuEST submissions will be evaluated across multiple metrics and trade-offs (exposed by authors): accuracy, speed, throughput, energy, cost of usage, etc. Furthermore, in contrast with other deep learning benchmarking challenges ReQuEST participants will be asked to submit a complete workflow artifact (see submission procedures) which encompasses toolchains, frameworks, algorithm, libraries, and target hardware platform; any of which can be fine-tuned, or customized at will by the participant to implement their optimization technique.

We strongly encourage artifact submissions for already published techniques since one of the ReQuEST goals is to prepare an open set of reference and optimized implementations of popular deep learning algorithms as portable and customizable workflows which can be easily reused, improved and build upon!

A ReQuEST artifact evaluation committee (AEC) will be tasked to independently reproduce and evaluate workflow submissions on compliant hardware platforms to reproduce results and aggregate them in a multi-objective public leaderboard. Due to the multi-faceted nature of the competition, submissions won’t be ranked according to a single metric, but instead the AEC will assess their Pareto optimality across two or more metrics exposed by authors. There won’t be a single ranking of submissions since this competition is multi-objective: it accounts for classification accuracy, inference latency, energy, ownership/usage cost and so on. As such, there won’t be a single winner, but better and worse designs based on their relative Pareto optimality (up to 3 design points allowed per submission).

The workshop co-located with ASPLOS 2018 will be the opportunity for the participants to share their research and implementation insights with the research community. A common academic and industrial panel will be held at the end of the workshop to discuss how to improve common SW/HW co-design methodology for deep learning and other real-world applications.

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Further details about deadlines, submission procedures and artifact evaluation:

* http://cKnowledge.org/request

Looking forward to your participation and submissions!

1st Tournament on Reproducible and Pareto-Efficient Deep Learning at ASPLOS`18