Deci’s deep learning platform paves the way for easy-to-build, top-performing, and cost-efficient deep neural networks ready for production at scale
Deci, the deep learning company dedicated to transforming the AI lifecycle, announced it has raised $9.1 million in a seed round led by Israel-based VC firm Emerge and global VC fund Square Peg. The company is building an AI-based platform that can automatically craft robust, scalable, and efficient deep neural network solutions ready for production at scale. Deci aims to help AI practitioners build the next generation of deep learning models.
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Advancements in AI, mainly powered by deep learning, have triggered groundbreaking innovations in medicine, manufacturing, transportation, communication, and retail. But, prolonged development cycles, high computing costs, and unsatisfying inference performance are making it nearly impossible for enterprises to productize AI. By harnessing AI to improve AI, Deci is automatically transforming models to be ready for effective production at scale. With Deci’s new deep learning platform, AI developers can achieve up to tenfold performance improvement on any task, be it machine vision, NLP, or audio, thus obtaining a significant competitive advantage.
“Deci is leading a paradigm shift in AI to empower data scientists and deep learning engineers with the tools needed to create and deploy effective and powerful solutions,” says Yonatan Geifman, CEO and co-founder of Deci. “The rapidly increasing complexity and diversity of neural network models make it hard for companies to achieve top performance. We realized that the optimal strategy is to harness the AI itself to tackle this challenge. Using AI, Deci’s goal is to help every AI practitioner to solve the world’s most complex problems.”
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Deci’s deep learning platform automatically gears up neural networks to become top-performing production-grade solutions on any hardware, including CPUs, GPUs, and special-purpose AI chips for edge and mobile. The platform is powered by Deci’s patent-pending AutoNAC (Automated Neural Architecture Construction) technology, which uses machine learning to redesign any model and maximize its inference performance – all while preserving its accuracy. The platform optimizes any given deep learning model and cuts its computing costs for any desired hardware.
“In contrast to most classical machine learning algorithms, in deep learning, it’s much easier to achieve shining out-of-sample accuracy with very large, over-parameterized, but very slow neural networks,” said Professor Ran El-Yaniv, Deci’s Chief Scientist, “Our AutoNAC performs a smart high-speed search across a huge set of neural network architectures to aggressively speedup runtime, while preserving accuracy, by optimizing the fit between the neural network structure, the user’s dataset, and the target computing hardware.”
