Arm Expands Flexible Access Program to Armv9 Edge AI Platform

Oct 20, 2025
Arm Holdings has expanded its Flexible Access licensing program to include its Armv9 edge AI platform, offering startups and device makers low-cost access to chip design tools for on-device AI development.

Arm Holdings Plc has expanded its Flexible Access licensing program to include its Armv9 edge AI platform, announced in a press release. The update gives startups and device manufacturers low-cost access to chip design tools, training, and resources for developing AI capabilities directly on devices.

The Armv9 edge AI platform features the Cortex-A320 CPU and Ethos-U85 NPU, supporting AI models with over one billion parameters. It also incorporates security features such as Pointer Authentication Code, Branch Target Identification, and Memory Tagging Extension to protect data and applications at the edge.

Through Flexible Access, companies can experiment with Arm’s technology at little or no upfront cost, paying license fees only when final designs are ready for production. The program currently includes over 300 members and has resulted in more than 400 completed chip designs. Participants include hardware firms like Raspberry Pi, SiMa.ai, and Hailo Technologies Ltd.

The Cortex-A320 will be available through the program in November 2025, with the Ethos-U85 processor following in early 2026.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Silicon Brief, Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

AI Chips & Datacenters Enterprise AI AI Brief AI Brief (X)
Free newsletter

Silicon Brief

Weekly coverage of AI hardware developments including chips, GPUs, cloud platforms, and data center technology.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.