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Do not just follow AI and systems revolution - partner with dividiti to lead it and accelerate AI research!
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Partners about our technology

Why are you here?

  • Are you working on a compute-intensive algorithm (e.g. for artificial intelligence, computer vision, computational science, etc.) that should perform optimally while balancing multiple objectives (e.g. speed, power consumption, accuracy, reliability)?
  • Do you need to optimize your solution to perform well across different inputs and hardware platforms (e.g. from IoT devices to data centers)?
  • Do you need to objectively compare multiple and continuously changing solutions to enable making smart decisions (e.g. research, development and device costs vs speed vs accuracy vs energy)?
  • Are you working on novel software and hardware that should perform well on realistic emerging workloads (e.g. deep learning)?
  • Are you working on a challenging research project but spend most of your time processing ever growing design and optimization data, and trying to somehow keep up with ever changing SW/HW, rather than innovating?

Are you struggling to adapt to a Cambrian AI/SW/HW explosion and technological chaos?

  • Tired of keeping up with the ever changing AI/SW/HW stack?
  • Lost in the ever growing number of design choices?
  • End up with under-performing and expensive software and hardware?
  • Never find time and resources to optimize your workload and tune models?
  • Lose ad-hoc research software and artifacts when leading researchers leave?
  • Spend more time on ad-hoc experimentation than on innovation?

dividiti can help!

We help our partners to automate, crowdsource and accelerate co-design of the efficient software/hardware/model stack for their emerging workloads including deep learning and AI across diverse devices from IoT to supercomputers using our award-winning Collective Knowledge technology.

Our revolutionary approach

Designing and optimizing computing solutions has become extremely challenging due to ever changing SW/HW stack, an exploding number of available choices and their interactions. Unfortunately, limited understanding of trade-offs, combined with the cost and time-to-market pressures, leads to few design and optimization choices being explored. This often results in over-provisioned (expensive) and under-performing (uncompetitive) products.

Over the past 15 years we have been leading several highly influential research projects on machine-learning based program optimization and run-time adaptation which enabled the world's first machine-learning based compiler and received multiple international awards.

At the same time, we suffered from all the above problems and eventually decided to develop a scientific research methodology, common workflow framework and public repository of knowledge (Collective Knowledge).

Collective Knowledge framework (CK) is a cross-platform open research SDK developed in collaboration with academic and industrial partners to share artifacts as reusable and customizable components with a unified Python JSON API (see open repository of AI artifacts); assemble portable and customizable experimental workflows (such as multi-objective AI/SW/HW autotuning and co-design); automate package installation across diverse hardware and environments; crowdsource and reproduce experiments across diverse platforms from IoT to supercomputers; unify predictive analytics and enable interactive articles. It helps out partners to reinvent computer engineering, enable sustainable and portable research software while adapting to a Cambrian AI/SW/HW chaos, and accelerate AI research.

We successfully used our approach to help Fortune 50 companies and SMEs achieve 2-20x performance increases, 30% energy reductions, 20% code size reductions, and automatic detection of software and hardware bugs for their business-critical use cases.

We now use our novel methodology and CK technology to help our partners initiate and lead groundbreaking, interdisciplinary and collaborative research projects on fair and reproducible benchmarking, optimization and co-design of emerging workloads such as AI across diverse hardware and inputs while dramatically reducing time to market and R&D costs by several orders of magnitude.

We also actively support Artifact Evaluation Initiatives at the leading computer systems conferences (CGO, PPoPP, PACT, RTSS, SC) to validate experimental results from published papers and improve artifact sharing and reuse!

You can find more out our long-term vision in the following recent publications: DATE'16 , CPC'15 , JSP'14.

Do not hesitate to contact us for more details!

  dividiti Ltd (IdeaSpace West)
3 Charles Babbage Road
Cambridge, CB3 0GT, UK
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