General Motors uses CK to crowd-benchmark and crowd-tune DNN engines and models. CK helps to easily swap different AI/ML frameworks, models, data sets as LEGO(tm) as described in the GM's presentation from the Embedded Vision Summit about CK-powered CNN/SW/HW benchmarking here:
CK is a modular framework to enable portable, customizable and reusable AI, ML and quantum workflows which can run across diverse models, data sets and platforms from IoT to supercomputers. It is developed with the help of the community and successfully used in reproducible benchmarking and optimization tournaments. CK is used to gradually modularize and automate existing benchmarks such as MLPerf (a broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms).

Arm is one of the first and main users of the Collective Knowledge Technology to design more efficient computer systems for emerging workloads such as deep learning across the whole SW/HW stack from IoT to HPC. See Arm's video from the Embedded Vision Summit about CK-powered collaborative DNN co-design, HiPEAC info (page 17) for more details about Arm and the cTuning foundation using CK to accelerate computer engineering, and a demo about connecting CK and Arm's workload automation, and building a representative set of applications and data sets from Arm TechCon'16.

Association for Computing Machinery (ACM) evaluates Collective Knowledge for sharing artifacts and experimental workflows as reusable and customizable components along with reproducible publications as a part of the Artifact Evaluation initiative.

A growing community of quantum computing experts and enthusiasts is building Quantum Collective Knowledge (QCK) - a unified workflow to benchmark, compare and optimize traditional and quantum algorithms, and forecast future developments in quantum computing. CK also supports regular, collaborative and reproducible Quantum hackathons while sharing all experimental results on a live scoreboard:
ReQuEST consortium is developing a scalable and CK-powered tournament framework, a common experimental methodology and an open repository to co-design and share the whole Pareto-efficient software/hardware stacks (accuracy, speed, energy, size, costs) for real-world applications and emerging workloads such as AI and ML across diverse models, data sets and platforms from cloud to edge. ReQuEST also promotes reproducibility of experimental results and reusability of systems research artifacts by standardizing evaluation methodologies and facilitating the deployment of efficient solutions on heterogeneous platforms. The organizers develop a supporting open-source and portable workflow framework to provide unified evaluation and real-time leader-board of shared solutions. ReQuEST promotes quality-awareness to the architecture and systems community, and resource-awareness to the applications community and end-users. We will keep and continuously update the best or original solutions close to a Pareto frontier in a multi-dimensional space of accuracy, execution time, power consumption, memory usage, resiliency, code/hardware/model size, costs and other metrics in a public repository.
Amazon evaluates CK to help users optimize performance, accuracy and speed of AI applications in AWS:

We collaborate with colleagues from TomTom on a model-driven approach for a new generation of adaptive libraries, while automating and crowdsourcing experiments and ML-based modeling using the Collective Knowledge framework.

We collaborate with Raspberry Pi foundation for a new educational initiative to teach students and researchers how to automatically optimize and test (autotuning, crowd-tuning and crowd-fuzzing) realistic workloads in terms of speed, size, energy usage, accuracy and costs across diverse software and hardware stack using CK workflow framework and open optimization repository.

We collaborate with the EcoSystem Lab from the University of Toronto led by Professor Gennady Pekhimenko to develop portable, customizable and reusable AI benchmarks based on the open-source TBD suite and the Collective Knowledge Workflow framework. The release is expected at the end of 2018.

The non-profit cTuning foundation and dividiti regularly help various international projects ( MILEPOST, CARP, PAMELA) and assist scientists in crowdsourcing and reproducing experiments, and developing customizable and sustainable research software powered by CK which can now survive in a Cambrian AI/SW/HW chaos or when leading researchers leave!

We help colleagues from the University of Edinburgh and Glasgow use CK to automate and crowdsource optimization of mathematical libraries and compilers.

We help colleagues from ENS Paris to automate and crowdsource polyhedral optimization using CK.

We assist colleagues from Hartree SuperComputing Center to use CK for customizable and sustainable experimental workflows and collaboratively optimize realistic workloads across various HPC systems.

Xored develops various open-source CK extensions since 2016 including DNN engine optimization front-end and Android app to crowdsource DNN benchmarking.

University of Cambridge colleagues use Collective Knowledge framework to develop sustainable software, accelerate research, automate experimentation and reuse artifacts. For example, portable and reproducible experimental workflow from the "Software Prefetching for Indirect Memory Accesses" article by Sam Ainsworth and Timothy M. Jones received a distinguished artifact award at CGO'17. Android app to crowdsource DNN benchmarking.

Alastair Donaldson's group uses CK to automate and crowdsource detection of compiler bugs (crowd-fuzzing of traditional, OpenCL and OpenGL compilers). TETRACOM project "CLmith in Collective Knowledge" won HiPEAC technology transfer award in 2016.

Non-profit cTuning foundation is the main coordinator and sponsor of all CK developments and CK-powered educational initiatives such as Artifact Evaluation. It also partnered with dividiti to facilitate technology transfer from academia to industry while boosting innovation in science and technology!

dividiti sponsors development of the open-source research SDK (CK), public repository of optimization knowledge, scientific methodology for computer engineering. dividiti brings together academia and industry to develop sustainable and customizable research software powered by CK, facilitate technology transfer in many of the above projects, initiate and lead innovative R&D projects powered by CK, and help scientists to adapt to a Cambrian AI/SW/HW explosion and technological chaos.