Workshop program

Important dates

• Artifact submissions due: February 12, 2018 AoE
  
• Artifact evaluation: February 13-February 21, 2018
• Author notification: February 22, 2018
• ASPLOS early registration deadline: February 23, 2018 (See ASPLOS registration and visa support)
• Workshop with presentations and discussions of winning workflows: March 24, 2018
• Final papers and artifacts for the Digital Library: mid April, 2018
• Report to our advisory board: end of April, 2018

Call for submissions

The 1st ReQuEST tournament is co-located with ACM ASPLOS'18 and will focus on optimizing the whole model/software/hardware stack for image classification based on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Unlike the classical ILSVRC where submissions are ranked according to their classification accuracy, however, ReQuEST submissions will be evaluated according to multiple metrics and trade-offs selected by the authors (e.g. accuracy, speed, throughput, energy consumption, hardware cost, usage cost, etc.) in a unified, reproducible and objective way using the Collective Knowledge framework (CK). Restricting the competition to a single application domain will allow us to test our open-source ReQuEST tournament infrastructure, validate it across multiple platforms and environments, and prepare a dedicated live scoreboard with results similar to this open SOTA scoreboard.

We encourage participants to target accessible, off-the-shelf hardware to allow our evaluation committee to conveniently reproduce their results. Example systems include:

• Server-class: AWS/Azure cloud instance, any x86-based desktop system.
• Mobile-class: Any Arm-based (e.g. NVIDIA Jetson TX2, Raspberry Pi 3, Xilinx PYNQ board), or Intel-Atom-based SoC development board, Android-based smartphone or tablets.
• IoT-class: Low-power Arm micro-controllers (e.g. Freescale FRDM KL03 Development Board).

If a submission relies on an exotic hardware platform, the participants can either provide restricted access to their evaluation platform to the artifact evaluation committee, or notify the organizers in advance (please try to give us at least 3 weeks notice) about their choice so that a similar platform can be acquired in time (assuming the cost is not prohibitive).

Example optimizations include:

• Design space exploration of model topologies, operators, activation functions, configurations.
• Hyper-parameter search and meta-learning techniques that help optimize accuracy and inference time.
• Comparison of different deep learning systems (for example TensorFlow vs. Caffe2 vs. CNTK vs. MXNet).
• Model optimizations that trade accuracy for speed or efficiency (e.g. reduced precision and model compression).
• Operator-level quantization, binarization or ternarization techniques to improve overall inference time (e.g. binary networks, XNOR nets).
• Library optimizations targeting deep learning operators on mobile systems (e.g. depthwise convolution).
• FPGA acceleration that takes advantage of narrow integer bitwidth.
• Software optimizations targeting GPU-less mobile/IoT systems.

We strongly encourage artifact submissions for already published optimization techniques since one of the ReQuEST goals is to prepare a reference (baseline) set of implementations of various algorithms shared as portable, customizable and reusable CK components with a common API. In fact, the ReQuEST submissions will be directly fed into pilot CK integrations with the ACM Digital Library.

Submission

We follow standard procedures for submitting and evaluating experimental workflows, as established at leading systems conferences including CGO, PPoPP, PACT and SuperComputing ("artifact evaluation"):

• Step 1: Share your experimental artifacts and workflows

  You should make all artifacts and experimental workflows publicly available via GitHub, GitLab, Bitbucket or similar, or pack them in a zip/tar archive or Docker/VM image. You should also provide instructions and scripts to build and run your workflows on a target platform, measure the characteristics and compare the results against a reference implementation.
  If you are already familiar with the open-source Collective Knowledge framework (CK), you are encouraged to convert your experimental workflows to to portable CK workflows. Such workflows can automatically set up the environment, detect required software dependencies, install missing packages and run experiments, thus automating artifact evaluation. (See some examples here.)
  If you are not familiar with CK, worry not! We will gladly help you convert your submission to CK during the evalution stage.

• Step 2: Submit an extended abstract with Artifact Appendix

  You should prepare an extended abstract (max 4 pages) using this ReQuEST LaTex template in the SIGPLAN conference style.
  Include your name, affiliation, and a brief description of your work (which can be novel or already presented elsewhere). Please also fill in the Artifact Appendix in the above template, including how to obtain your artifacts and workflows. Provide a detailed specification of your experimental workflow, a list of optimization metrics (speed, accuracy, energy, costs, etc.) and the expected results (which the reviewers will need to independently validate).
  Please submit your extended abstract as a PDF via the ReQuEST HotCRP website. Please contact the organizers if your encounter any problems.

Evaluation

ReQuEST is backed by the ACM Task Force on Data, Software, and Reproducibility in Publication and uses the standard artifact evaluation methodology. Artifact evaluation is single blind (see PPoPP, CGO, PACT, RTSS and SuperComputing). Reviews will be performed by the organizers and volunteers ("reviewers"), and can be made public upon the authors' request (see ADAPT). Quality and efficiency metrics will be collected for each submission, and displayed on a live ReQuEST scoreboard similar to this open CK repository.

• Step 1: Collaborate on converting your workflows to CK

  If your submission is not in the CK format, we will help you to add a portable CK workflow for your algorithm while reusing available CK packages and modules shared by the community (see CK Getting Started Guides, CK ReQuEST workflow example to explore MobileNets on Arm GPUs, shared CK packages CK software detection plugins, reusable CK modules (unified scripts and tool wrappers) and CK repositories with AI/ML workflows). You may choose how to communicate with us during this step: either privately via HotCRP, semi-privately via a dedicated Slack channel with all authors and reviewers, or, preferably, publicly via CK slack channel or the CK mailing list (thus making the community immediately aware of your artifact).

• Step 2: Collaborate on validating your results

  We will form a ReQuEST artifact evaluation committee (AEC) from the organizers and volunteers ("reviewers"). The AEC task is to objectively evaluate submissions on appropriate hardware platforms, reproduce results and aggregate them on a multi-objective public scoreboard. AE will be a friendly and interactive process between the authors and the reviewers, with the goal of making the artifacts as useful as possible for the community. For example, the reviewers may encounter some unexpected problems, and ask the authors for help to fix them.
  Again, the authors can communicate with the reviewers privately via HotCRP, semi-privately via Slack, or publicly by opening tickets in shared repositories (see examples 1 and 2) and/or via the CK mailing list. If any of the organizers submit their workflows (mainly to provide reference implementations), their submissions will go through public evaluation.

• Step 3: Collaborate on visualizing your results on a public scoreboard

  Due to the multi-faceted nature of the competition, submissions will not be ranked according to a single metric (as this often results in over-engineered solutions), but instead the AEC will assess their Pareto optimality on two or more metrics exposed by the authors. As such, there will not be a single winner, but rather better and worse designs based on their relative Pareto optimality (up to 3 design points allowed per each submission). We will collaborate with the authors to correctly visualize the results and SW/HW/model configurations on a public scoreboard while grouping them according to certain categories of their choice (e.g. embedded vs. server). A unique submission may define a category in its own right. To win, the results of an entry will normally lie close to the Pareto-optimal frontier in its category. However, a winning entry can be also praised for its originality, reproducibility, adaptability, scalability, portability, ease of use, etc.

Presentation

• Step 1: Present at the ReQuEST workshop at ASPLOS'18

  We will announce accepted SW/HW/model configurations at the end of February, and invite the authors to present their work at the 1st ReQuEST workshop co-located with ASPLOS 2018 (ACM conference on Architectural Support for Programming Languages and Operating Systems, which is the premier forum for multidisciplinary systems research spanning computer architecture and hardware, programming languages and compilers, operating systems and networking). This will give the authors an opportunity to share their research and implementation insights with the research community as well as discuss future R&D directions.
  A common academic and industrial panel will be held at the end of the workshop to discuss how to improve the common SW/HW co-design methodology and infrastructure for deep learning and other real-world workloads.

• Step 2: Publish in the ACM Digital Library

  The authors of the winning submissions will publish their extended abstracts with an Artifact Appendix and related artifacts in the ACM Digital Library (even if their techniques have already been published, since the workshop focuses on validated and reusable artifacts!) Furthermore, we have partnered with ACM to award "available / reusable / replicated" badges to all the winning artifacts. This will make them discoverable via the ACM Digital Library (check this out by selecting "Artifact Badge" for a field and then select any badge you wish in the ACM DL advanced search)!