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Deci, a deep-learning software maker that uses AI templates designed to create AI-based applications, today launched v2.0 of its development platform, which it claims speeds the way for developers to build, optimize and deploy computer vision models.
The term “speed” and AI application development are rarely used in the same sentence, but by using this platform, resulting AI models can be more swiftly prepared to run on any hardware and environment, including cloud, edge and mobile – with accuracy and high runtime performance, Deci CEO and co-founder Yonatan Geifman said in a media advisory. This is because much of the grunge work has been eliminated by the Deci series of DeciNet templates made available in the v2.0 platform.
Using Deci, the company says, AI developers can achieve improved inference performance and efficiency to enable effective deployments on resource-constrained edge devices, maximize hardware use and reduce training and inference cost, Geifman said. The entire development cycle is shortened – saving upfront costs – and the uncertainty of how the model will deploy on the inference hardware is eliminated, he said.
Deci’s platform, powered by its proprietary neural architecture search (NAS) engine called AutoNAC (Automated Neural Architecture Construction), is designed to enable AI developers to automatically build efficient computer vision models that provide previously tested accuracy for required inference hardware, speed, size and targets. DeciNet models generated by Deci outperform other known state-of-the-art architectures by a factor of three times to 10 times, Geifman said.
AI developers generally have struggled to develop production-ready deep-learning models for deployment in a reasonable amount of time. These challenges can largely be attributed to the AI efficiency gap facing the industry, in which algorithms are growing more powerful and complex, but available compute power isn’t keeping pace with demand. This gap also creates financial barriers by making the deep-learning development and processing more cumbersome and expensive, Geifman said.
While NAS has been presented as a potential solution to automate the design of superior artificial neural networks that can outperform manually designed architectures, the resource requirements to operate such technology are excessive for most companies. So far, NAS has only been successfully implemented by tech giants with large AI teams such as Google, Facebook and Microsoft and in the academic community, indicating its impracticality for the vast majority of developers.
Developers can start their projects with the DeciNet pretrained and optimized models generated by the AutoNAC engine for a wide range of hardware and computer vision tasks or use the AutoNAC engine to generate more custom architectures that are tailored for their specific use-cases, Geifman said.
In addition, the platform supports teams with the wide range of tools required to develop deep learning-based applications. These include a hardware-aware PyTorch model to easily select and benchmark models and hardware, SuperGradients — an open-source training library housed on GitHub with proven recipes for faster training, automated runtime optimizations, model packaging and more, Geifman said.
With Deci’s v2.0 platform, AI developers can accomplish the following:
Deci’s platform includes these three tiers:
Deci competes in the market against Datagen, Reverie, Simerse, Zumo Labs, CVEDIA, Masterful AI, Mostly AI, OneView, Synthesis AI and Sky Engine.
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