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Mindtech Announces Enhanced Scalability Support for Chameleon 


Mindtech (PRNewsfoto/Mindtech Global Ltd)


November 15th, 2019​​​​​​​​​​​​​​

LONDON, Nov. 15, 2019 /PRNewswire/ -- Mindtech Global Ltd, a UK based start-up, has announced container support to allow running multiple instances of their Chameleon synthetic data generation tool on clustered platforms.  This approach enables full use of available compute resources leading to a significant reduction in training cycle times for neural networks using Synthetic data.

Scalability by design

Mindtech's Chameleon was built from the outset with the capability to scale for generating large amounts of data to meet the demands of training visual neural networks for AI systems.  The simulator incorporates a scripting engine, enabling easy capabilities for repeated simulation runs, changing single variables at a time.  The scenario editor is intended to allow the quick and easy creation of multiple different sequences, to create the data required for the intended use case.

Exploiting scalability

The Scenario editor and simulation scripting engine between them ensure that the Chameleon tools can easily be used to generate huge amounts of data.  To allow the user to effectively exploit this requires that simulation and rendering hardware be fully utilised.  To achieve this the tools were designed to fully employ all resources available, taking advantage of multi-thread processors and GPU accelerators available on the system hosting the simulator.  To further enhance this capability, Mindtech has updated the simulator and encapsulated it in a containerised form, to allow use of such technologies as Singularity and Docker.  This in turns allows deployment within a clustered environment like Kubernetes.  This new functionality allows for deployment at server and cloud scale, enhancing the ability to utilise compute resources available.  

The need for scalability

The creation of the virtual worlds, used to create Mindtech's high quality synthetic data, requires the use of sophisticated 3D rendering techniques, as well as significant processor power, delivered by GPU accelerators, to help ensure the virtual world behaves in a realistic, "life-like" manner.  Each training task for a neural network will require hundreds of thousands or even millions of images, each individually rendered, and annotated for the training task.  The use of containers allows the user to run multiple simulation runs in parallel, vastly reducing the time to create data, and shortening the overall training cycle for neural networks.

Says Ogi Brkic, Corporate Vice President and General Manager of the Data Center GPU Business Unit at AMD, Radeon Technology Group: "The combination of the latest AMD EPYC™ 7002 Series Processor and Radeon Instinct™MI50 GPU accelerator represents the best of what's possible for modern AI workloads. Mindtech's Chameleon leverages this powerful compute platform to accelerate synthetic data generation and to help reduce training cycle times with a containerized approach."


Mindtech's Chameleon AI Tools scalability using containers will be shown on the AMD stand at Supercomputing 2019, November 18-21, Denver, Colorado. 


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AMD, the AMD logo, EPYC, Radeon Instinct, and combinations thereof are trademarks of Advanced Micro Devices, Inc.

SOURCE Mindtech Global Ltd

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September 17th, 2019


Mindtech Introduce Chameleon Synthetic Data Generator and AI Tools for Effective Training of AI-Based Solutions

LONDON, SEPT. 17 - Mindtech Global Ltd, a UK based start-up, has announced the availability of Mindtech Chameleon Simulator, creating synthetic vision datasets for training neural networks, and Mindtech Chameleon AI Tools, providing end to end data management for deep learning systems.

Synthetic Datasets

Effective training of neural networks for visual processing requires very large datasets i.e. images with ground-truth annotations; access to these datasets is a major barrier to entry for most companies. The Chameleon Simulator economically creates unlimited, unbiased training data.  A wide range of fully accurate annotations, including pixel perfect masks, precise range data, and derivatives such as velocity are easily generated.

Real-world data has many limitations which are overcome by Chameleon’s synthetic data: modelling of difficult edge cases, accurate synthesis of customer’s target system (lens, sensor, processing distortions) and enabling datasets free of privacy/GDPR issues.

Market and application optimized

Optional market-centric packs have been created to allow customers to rapidly create environments suitable for automotive, unsupervised machines, retail and security scenarios. Custom packs are created on demand.

Dataset management

Chameleon AI Tools simplify data wrangling tasks. They manage the merging, verification and augmentation of datasets for use in industry standard frameworks such as TensorFlow and Caffe2.  The tools report and visualize relevant statistics for results analysis.

Outstanding results

The use of synthetic data improves accuracy of neural networks, can actively reduce bias and vastly reduce the amount of “real” data required, saving time and money.

Says Chris Longstaff, VP Product Management, Mindtech: “Chameleon Tools enable everyone to bring innovative solutions to market.  The ability to reduce bias is an important part of our company’s vision to allow for the ethical use of AI.”

“Sufficient quantities of diverse, high-quality, labelled images are critical for training and validating today’s visual AI solutions,” said Jeff Bier, founder of the Embedded Vision Alliance. “Synthetic images, such as those created by Mindtech's Chameleon toolset, can ease the challenge of sourcing large quantities of labelled real-world images.  This approach is especially interesting for applications in which developers require images that are difficult to capture in the physical world – for example because the images would be expensive to stage.”


Mindtech’s Chameleon AI Tools are available today. 


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