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Developing efficient software and hardware has never been harder whether itis for a tiny IoT device or an Exascale supercomputer. Apart from the evergrowing design and optimization complexity, there exist even more fundamentalproblems such as lack of interdisciplinary knowledge required for effectivesoftware/hardware co-design, and a growing technology transfer gap betweenacademia and industry.  We introduce our new educational initiative to tackle these problems bydeveloping Collective Knowledge (CK), a unified experimental framework forcomputer systems research and development. We use CK to teach the community howto make their research artifacts and experimental workflows portable,reproducible, customizable and reusable while enabling sustainable R&D andfacilitating technology transfer. We also demonstrate how to redesignmulti-objective autotuning and machine learning as a portable and extensible CKworkflow. Such workflows enable researchers to experiment with differentapplications, data sets and tools; crowdsource experimentation across diverseplatforms; share experimental results, models, visualizations; gradually exposemore design and optimization choices using a simple JSON API; and ultimatelybuild upon each other's findings.  As the first practical step, we have implemented customizable compilerautotuning, crowdsourced optimization of diverse workloads across Raspberry Pi3 devices, reduced the execution time and code size by up to 40%, and appliedmachine learning to predict optimizations. We hope such approach will helpteach students how to build upon each others' work to enable efficient andself-optimizing software/hardware/model stack for emerging workloads.

A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques