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All our researchers and engineers has either MS or PhD degrees in computer engineering, computer science, physics and machine learning from the leading universities including Cambridge and Edinburgh. Such an exceptional scientific and engineering background, interdisciplinary knowledge, strong commitment and motivation, combined with our unique Collective Knowledge approach helps us set up and lead highly innovative research projects of any complexity and duration!

We currently focus on automating and accelerating AI/SW/HW co-design to enable efficient, self-optimizing and self-learning systems. We use Collective Knowledge framework and repository to crowdsource AI/SW/HW autotuning in terms of speed, accuracy, energy, memory, size and costs across diverse hardware, environments and realistic data sets, and combine it with machine learning and run-time adaptation (DATE'16, CPC'15, JSP'14. ):

In the past, dividiti founders participated in a number of highly successful research international projects including MILEPOST which produced machine learning based self-optimizing compiler as well as a public repository of optimization knowledge considered by IBM to be the first in the world!

In 2017 we received a test of time award for the ACM CGO'07 research paper that led to creating Collective Knowledge - this annual award recognizes outstanding papers published at the ACM/IEEE International Symposium on Code Generation and Optimization (CGO) one decade earlier, whose influence is still strong today!

Publications from our team:

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3 Charles Babbage Road
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