Open Ended And Ai Generating Algorithms In The Era Of Foundation Models Jeff Clune
Jeff Clune On LinkedIn: Open-ended And AI-generating Algorithms In The ...
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%2C with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry and research community have witnessed a large number of new applications%2C based on those foundation models. Such applications include question and answer%2C customer services%2C image and video generation%2C and code completions%2C among others. However%2C as the number of model parameters reaches to hundreds of billions%2C their deployment incurs prohibitive inference costs and high latency in real-world scenarios. As a result%2C the demand for cost-effective and fast inference using AI accelerators is ever more higher. To this end%2C our tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators. Beginning with an overview of basic Transformer architectures and deep learning system frameworks%2C we deep dive into system optimization techniques for fast and memory-efficient attention computations and discuss how they can be implemented efficiently on AI accelerators. Next%2C we describe architectural elements that are key for fast transformer inference. Finally%2C we examine various model compression and fast decoding strategies in the same context.?resize=650,400 "Inference Optimization Of Foundation Models On AI Accelerators | AI ...")
Inference Optimization Of Foundation Models On AI Accelerators | AI ...
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2023 - On The Opportunities And Challenges Of Foundation Models For ...
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Open-ended and AI-generating Algorithms in the Era of Foundation Models: Research Talk by Jeff Clune
Open-ended and AI-generating Algorithms in the Era of Foundation Models: Research Talk by Jeff Clune
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%2C with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry and research community have witnessed a large number of new applications%2C based on those foundation models. Such applications include question and answer%2C customer services%2C image and video generation%2C and code completions%2C among others. However%2C as the number of model parameters reaches to hundreds of billions%2C their deployment incurs prohibitive inference costs and high latency in real-world scenarios. As a result%2C the demand for cost-effective and fast inference using AI accelerators is ever more higher. To this end%2C our tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators. Beginning with an overview of basic Transformer architectures and deep learning system frameworks%2C we deep dive into system optimization techniques for fast and memory-efficient attention computations and discuss how they can be implemented efficiently on AI accelerators. Next%2C we describe architectural elements that are key for fast transformer inference. Finally%2C we examine various model compression and fast decoding strategies in the same context.?resize=91,91 "Inference Optimization Of Foundation Models On AI Accelerators | AI ...")
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