--Accurate graph similarity is critical for knowledge transfer in VLSI design, enabling the reuse of prior solutions to reduce engineering effort and turnaround time. We propose Pieceformer, a scalable, self-supervised similarity assessment framework, equipped with a hybrid message-passing and graph transformer encoder . T o address transformer scalability, we incorporate a linear transformer backbone and introduce a partitioned training pipeline for efficient memory and parallelism management. Evaluations on synthetic and real-world CircuitNet datasets show that Pieceformer reduces mean absolute error (MAE) by 24.9% over the baseline and is the only method to correctly cluster all real-world design groups. We further demonstrate the practical usage of our model through a case study on a partitioning task, achieving up to 89% runtime reduction. The increasing complexity of VLSI systems--driven by advanced packaging technologies, shrinking technology nodes, and rapid product cycles--has placed enormous pressure on modern semiconductor design workflows. Meanwhile, tasks such as synthesis, placement, routing, and verification remain highly iterative, computationally expensive, and dependent on deep domain expertise. As a result, there is a growing need for automated methods that can effectively reuse knowledge from existing, optimized designs to accelerate new design efforts.

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Stable Diffusion is a deep learning model that allows you to generate realistic, high-quality images and stunning art in just a few seconds. Although creating impressive images can find use in industries ranging from art to NFTs and beyond, today we also expect AI to be personalizable. Today, we announce that you can personalize the image generation model to your use case by fine-tuning it on your custom dataset in Amazon SageMaker JumpStart. This can be useful when creating art, logos, custom designs, NFTs, and so on, or fun stuff such as generating custom AI images of your pets or avatars of yourself. In this post, we provide an overview of how to fine-tune the Stable Diffusion model in two ways: programmatically through JumpStart APIs available in the SageMaker Python SDK, and JumpStart's user interface (UI) in Amazon SageMaker Studio. We also discuss how to make design choices including dataset quality, size of training dataset, choice of hyperparameter values, and applicability to multiple datasets.

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. JumpStart also offers example notebooks that use Amazon SageMaker features like spot instance training and experiments over a large variety of model types and use cases. These example notebooks contain code that shows how to apply ML solutions by using SageMaker and JumpStart. They can be adapted to match to your own needs and can thus speed up application development. Recently, we added 10 new notebooks to JumpStart in Amazon SageMaker Studio.

We're thrilled to announce that Stability AI has selected AWS as its preferred cloud provider to power its state-of-the-art AI models for image, language, audio, video, and 3D content generation. Stability AI is a community-driven, open-source artificial intelligence (AI) company developing breakthrough technologies. With Amazon SageMaker, Stability AI will build AI models on compute clusters with thousands of GPU or AWS Trainium chips, reducing training time and cost by 58%. Stability AI will also collaborate with AWS to enable students, researchers, startups, and enterprises around the world to use its open-source tools and models. "Our mission at Stability AI is to build the foundation to activate humanity's potential through AI. AWS has been an integral partner in scaling our open-source foundation models across modalities, and we are delighted to bring these to SageMaker to enable tens of thousands of developers and millions of users to take advantage of them. We look forward to seeing the amazing things built on these models and helping our customers customize and scale their models and solutions."

Today we are excited to announce that AI21 Jurassic-1 (J1) foundation models are available for customers using Amazon SageMaker. Jurassic-1 models are highly versatile, capable of both human-like text generation, as well as solving complex tasks such as question answering, text classification, and many others. You can easily try out this model and use it with Amazon SageMaker JumpStart. JumpStart is the machine learning (ML) hub of SageMaker that provides access to foundation models in addition to built-in algorithms and end-to-end solution templates to help you quickly get started with ML. In this post, we walk through how to use the Jurassic-1 Grande model in SageMaker.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fifth in a series on using JumpStart for specific ML tasks. In the first post, we showed how you can run image classification use cases on JumpStart.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fourth in a series on using JumpStart for specific ML tasks. In the first post, we showed how to run image classification use cases on JumpStart.

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The Veterans Affairs Department plans to contract app developer JumpStart to build a platform that uses artificial intelligence and the user's own body to improve the physical therapy process. The app, called the AI Based Physical Therapy App, or AIPTA, will be designed to predict and mitigate repetitive stress-related injuries, according to a request for information posted to SAM.gov. The app will use an adaptive user interface to receive input from wearable devices that monitor various health diagnostics. The VA expressed enthusiasm with a particular vendor, JumpStart, and noted that it "was rated to have the highest AI-based physical therapy biofeedback and analytical capabilities as related to physical therapy and rehabilitation," during a tech sprint held by the agency's National Artificial Intelligence Institute from December to June. "VA has already vetted several other AI health applications but have not found any vendors with similar capabilities," the notice read.

In order to implement your artificial intelligence strategy, you need to understand how AI works, find out potential business use cases, gather high quality data and check whether your team is skilled. To make the most out of AI offerings and to drive competitive advantage, you should jumpstart your artificial intelligence strategy by following the guidelines mentioned in this blog post. As the pace of digital transformation goes up, business leaders should prepare to make faster, smarter, and more tangible decisions around the continuously growing data. Hence, companies need the assistance of artificial intelligence solutions that will allow them to make more, real-time, and accurate decisions for their business with maximum efficiency. But, for leveraging artificial intelligence in an organization, there should be an effective artificial intelligence strategy in place, which acts as a roadmap that guides business leaders to use the technology for the right business use cases.