![]() ![]() I was also able to catch awesome demos from Awiros, Icuro, and a hands-on OpenVINO walkthrough from Intel AI Evangelist Paula Ramos. MindsDB, on the other hand, presented OpenVINO in a non-imaging use case by folding it into its in-database machine learning platform, where developers can create applications like a real estate cost estimator on the fly. The Deci AI neural network optimizer, for example, doubles down on OpenVINO to improve inferencing performance by 4x compared to the toolkit alone. From there, a video game let participants drive a digital twin of the rover around Mars, where it could encounter and learn about real rovers like the Perseverance.īut Innovation 2022 exhibitions didn’t just include AI for end users. It showcased how this could work at Innovation 2022, where an edge AI object recognition stack identified Legos that comprised a rover and instructed users how to assemble them properly. ![]() While those use cases are on the more advanced end of the spectrum, meldCX has recognized the need to educate the next generation of technologists on AI fundamentals. And while a picture archive and communications system (PACS) itself isn’t unique, JelloX showed how its MetaLite PACS with Intel-powered AI can integrate with radiology and other hospital systems to create AI-enabled imaging and digital pathology platforms. via at the edge, Eigen Innovations demonstrated how its OpenVINO-based software stack revolutionizes automated optical inspection (AOI) for manufacturers by integrating real-time control system and environmental data with AI inferences. Regardless of where you are in the development lifecycle, where your application sits on the #edge-to-#cloud continuum, or even your skill set, technology is evolving to simplify and accelerate your #AI experience. This included PreciTaste, whose object recognition software helps Chipotle monitor food stock so there’s always enough on hand. Many Intel partners showcased their innovative edge AI solutions built on OpenVINO. ![]() Democratizing AI, Live at the Edgeīut Geti wasn’t the only example of AI innovation at the event. The platform is accessible to data scientists, domain experts, and AI developers alike, who can leverage it to output production-ready deep-learning models in formats like PyTorch, TensorFlow, or as neural networks that can be optimized by the popular Intel ® Distribution of OpenVINO ™ Toolkit. Intel Geti is a new AI platform designed to streamline the time-consuming process of dataset labeling by offering an intuitive environment and annotation tools that let computer vision model training commence with as few as 20 images. Beneath was a whole lot of AI innovation, headlined by Intel ® Geti ™, which Gelsinger announced in his keynote. And one of those optimizations, CEO Pat Gelsinger says, is a SKU that will clock at 6 GHz!Īnd that was just the tip of the iceberg when it came to Innovation 2022 announcements. The latest chipsets continue the hybrid Performance and Efficiency core architecture of the previous generation but add optimizations that yield 15 percent better single-threaded and 41 percent higher multi-threaded performance. Now the company is on its 13th Generation Intel ® Core ™ processors, which was just announced at Intel ® Innovation 2022. It’s hard to believe, but the company was just rolling out its 7th Generation Intel ® Core ™ processors back then. Unlike specialized systems, Alpa also generalizes to models with heterogeneous architectures and models without manually-designed plans.Before last week, the last Intel ® event I attended in person was the Intel ® Developer Forum (IDF) in 2016. Our evaluation shows Alpa generates parallelization plans that match or outperform hand-tuned model-parallel training systems even on models they are designed for. Alpa implements an efficient runtime to orchestrate the two-level parallel execution on distributed compute devices. Alpa designs a number of compilation passes to automatically derive efficient parallel execution plans at each parallelism level. Based on it, Alpa constructs a new hierarchical space for massive model-parallel execution plans. Alpa distributes the training of large DL models by viewing parallelisms as two hierarchical levels: inter-operator and intra-operator parallelisms. They do not suffice to scale out complex DL models on distributed compute devices. Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations. Alpa automates model-parallel training of large deep learning (DL) models by generating execution plans that unify data, operator, and pipeline parallelism. ![]()
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