We help our clients and partners quickly develop sustainable research software powered by Collective Knowledge framework; accelerate critical research (systems, AI) by several orders of magnitude; crowdsource and reproduce experiments across diverse platforms; take advantage of the unique open repository of reusable, portable, customizable and optimized AI artifacts; enable collaborative AI/SW/HW co-design from IoT to supercomputers; perform competitive analysis and further optimization of numerous AI/SW/HW solutions in terms of speed, accuracy, energy, memory, size and costs; enhance digital libraries and enable reproducible articles; accelerate knowledge discovery particularly for Artificial Intelligence; facilitate technology transfer from academia; reduce time to market for your new products, and minimize all R&D costs.
We have been successful in our business already helping Fortune 50 companies, SMEs and universities to set up and lead many highly innovative R&D projects all over the world. We were finalists of the Cambridge Wireless Discovering Start-Ups 2016 competition. We also sponsor various academic activities at the leading ACM and IEEE conferences to enable truly collaborative, reproducible and reusable research.
We now lead the open CK AI consortium and plan to raise the next level of funding in 2018 to considerably enhance our unique technology to crowdsource deep learning and optimization across billions of diverse devices - contact us if you like to support this development and join this exciting long-term adventure!
Anton has been working in the area of programming languages and tools for 15 years, both as a researcher and engineer, primarily focusing on productivity, efficiency and portability of programming techniques for heterogeneous systems. Anton founded dividiti to pursue his vision of efficient, reliable and cheap computing everywhere.
In 2010-2015, Anton led development of GPU Compute programming technologies for the ARM Mali GPUs, including production (OpenCL, RenderScript) and research (EU-funded CARP project) compilers. He was actively involved in championing technology transfer, educating professional and academic developers, engaging with partners and customers, contributing to open-source projects and standardization efforts. In 2008-2009 he was a post-doctoral research associate at Imperial College London.
Grigori has an interdisciplinary background in computer engineering, physics and predictive analytics with more than 20 years of R&D experience. He has pioneered collaborative, reproducible and machine-learning based approaches to automatically optimize and co-design efficient software and hardware.
While a tenured research scientist at INRIA Grigori developed a machine-learning based compiler (MILEPOST GCC) combined with a public optimization repository (cTuning.org) considered by IBM to be the first in the world. He recently received CGO'17 test of time award for related research. In 2010-2011, Grigori was on leave from INRIA invited to help establish the Intel Exascale Lab in France while serving as the head of its program optimization and characterization group. As founder of the nonprofit cTuning Foundation, Grigori is also leading a new artifact evaluation initiative for PPoPP, PACT and CGO, the premier ACM conferences on parallel programming, architecture and code generation, which aims to encourage sharing of code and data to enable reproducible systems research and engineering.
Grigori co-founded dividiti to focus on transferring to industry his unique open-source Collective Knowledge technology to structure, share, cross-link and reuse all available artifacts (code, data, models) and optimization knowledge via public or private CK repositories. All aggregated information is then extrapolated to help dividiti's customers and partners build faster, cheaper, smaller, more power efficient, accurate and reliable self-optimizing computer systems from IoT to supercomputers for emerging workloads such as AI.