Deci, the deep learning company harnessing AI to build AI, announced the discovery of their family of industry-leading image classification models dubbed DeciNets, giving developers access to more deep learning algorithms and innovations.
Deci’s proprietary Automated Neural Architecture Construction (AutoNAC) technology discovered DeciNets using roughly two orders of magnitude less computing power than Google-scale Neural Architecture Search (NAS) technologies, the latter having been used to uncover well-known and powerful neural architectures like EfficientNet.
The race for improved accuracy and performance on new and more challenging prediction tasks, in conjunction with the availability of increasingly more powerful hardware and big data, has led to a push for larger deep learning models with increasing algorithmic complexity. These have essentially become unsustainable for cost-effective inference operations in production.
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To date, NAS has really been only successfully implemented by tech giants like Google, Microsoft and in the confines of academia, proving its impracticality for the vast majority of developers.
In order to solve this problem, Deci developed AutoNAC, the first commercially viable NAS enabling developers to automatically design and build deep learning models that outperform other known state-of-the-art architectures.
Setting parameters of their choice to tackle a specific task (e.g., classification, detection, segmentation), developers can apply AutoNAC to their dataset to obtain optimized models ready for production at scale on their target inference hardware.
Unlike other NAS technologies, AutoNAC is hardware-aware, meaning that it can squeeze maximum performance out of any hardware and deploy models in any environment (cloud, edge, mobile).
“Deep learning is powering the next generation of computing- without higher performing and more efficient models that seamlessly run on any hardware, consumer technologies we take for granted everyday will reach a barrier,” said Yonatan Geifman, co-founder and CEO of Deci. “Deci’s ‘AI that builds AI’ approach is crucial in unlocking the models needed to unleash a new era of innovation, empowering developers with the tools required to transform ideas into revolutionary products.”
Deci applied AutoNAC on several tasks to optimise models over several inference processors including Nvidia’s T4 GPU and Nvidia’s Jetson Xavier NX edge GPU.
For image classification using the standard ImageNet benchmark dataset, AutoNAC discovered DeciNets. Critically, Deci outperformed other platforms using much less computation when generating its DeciNet, demonstrating the potential for developers to obtain powerful, fully optimised neural models ready for production at scale without requiring heavy resources in the process. DeciNets significantly minimises the tradeoff between accuracy and inference performance, effectively outperforming any known open-source neural net currently on the market, including EfficientNets and MobileNets.
Deci’s AutoNAC technology is already serving multiple customers across industries in production environments.
