Arm recruiting Amazon’s top IC developer, the smart glasses market, whistles being blown on the Turing Institute, Haylo Labs buying Plessey Semiconductors, and big developments at Intel…

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Eighteen years ago, the foundries were looking for better prices – a trend that has led to today’s $30k wafer. The foundries are getting ready to put the squeeze on …

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Power Integrations is showing off its GaN technology with a PSU development kit for solar-powered racing cars, that it is offering free to such teams. “The kit is inspired by …

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SpinLaunch, the kinetic space launch company, announces it has raised $30 million in funding. This will accelerate the deployment of its Meridian Space constellation. The company says it is building …

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Toshiba has squeezed a photo relay with 1,500V output mosfets into an 8 x 10mm surface-mount package. TLX9161T is aimed at electric vehicle batteries, and turns on or off in …

The post 1,500V 30mA photo-relay in 8x10mm package appeared first on Electronics Weekly .

Diodes has announced four automotive buck converters for 48V rail point-of-load use. The parts are similar, but rated at 5 to 100V or 5 to 80Vin, and 2.5A or 3.5A …

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AWS IoT Core now supports customer-managed keys (CMK) through AWS Key Management Service (KMS), enabling you to encrypt data stored in IoT Core with your own encryption keys. This enhancement gives you greater control over your encryption key lifecycle, including creation, rotation, monitoring, and deletion. Moreover, when you opt for CMK, the service handles the transition by automatically re-encrypting existing data while helping to maintain uninterrupted IoT operations. Operating within the AWS shared responsibility model , this security enhancement helps organizations meet their security requirements without impacting their IoT applications.

This feature is now available in all AWS Regions where AWS IoT Core is supported. For details, visit the AWS IoT Core documentation .

Today, AWS announces the general availability of Neuron SDK 2.25.0, delivering improvements for inference workloads and performance monitoring on AWS Inferentia and Trainium instances. This latest release adds context and data parallelism support as well as chunked attention for long sequence processing in inference, and updates the neuron-ls and neuron-monitor APIs with more information on node affinities and device utilization, respectively.

This release also introduces automatic aliasing (Beta) for fast tensor operations, and adds improvements for disaggregated serving (Beta). Finally, it provides upgraded AMIs and Deep Learning Containers for inference and training workloads on Neuron.

Neuron 2.25.0 is available in all AWS Regions where Inferentia and Trainium instances are offered.

To learn more and for a full list of new features and enhancements, see:

• AWS Neuron 2.25.0 release notes
• Trn2 Instances
• Trn1 Instances
• Inf2 Instances

The Count Tokens API is now available in Amazon Bedrock, enabling you to determine the token count for a given prompt or input being sent to a specific model ID prior to performing any inference.

By surfacing a prompt’s token count, the Count Tokens API allows you to more accurately project your costs, and provides you with greater transparency and control over your AI model usage. It allows you to proactively manage your token limits on Amazon Bedrock, helping to optimize your usage and avoid unexpected throttling. It also helps ensure your workloads fit within a model’s context length limit, allowing for more efficient prompt optimization.

At launch, the Count Tokens API will support Claude models, with the functionality available in all regions where these models are supported . For more information about this new feature, including supported models and use cases, visit the Count Tokens API documentation .

Amazon SageMaker Unified Studio now offers a simplified file storage option in projects, providing data workers with an easier way to collaborate on their analytics and machine learning workflows without depending on Git. You can now choose between Git repositories (GitHub, GitLab or Bitbucket Cloud) or Amazon Simple Storage (S3) buckets for sharing code files between the various members of a project. While S3 is the default option, customers who want to use Git can still continue to have the same experience as they currently do.

With this launch, customers will see a consistent view of their files irrespective of the tool they are working in across SageMaker Unified Studio (such as JupyterLab, Code Editor or SQL query editor) making it easy to create, edit and share code. The S3 file storage option operates on a “last write wins” principle and supports basic file versioning when enabled by administrators. This option is particularly beneficial for data science teams who want to focus on their analytics and machine learning work without managing Git operations, while still maintaining a collaborative workspace for their project artifacts.

This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more about storage options in SageMaker Unified Studio projects, see Managing Project Files in the Amazon SageMaker Unified Studio User Guide .