Gen-AI-Today

GenAI TODAY NEWS

Free eNews Subscription

Following its $68M Funding Round, Axelera AI Continues to Accelerate Next-Gen Intelligence

By Alex Passett

Let’s talk about Axelera AI.

Simplifying AI at the edge. Accelerating computer vision.” (A pretty clear tagline, if you ask us.)

In this vein, Axelera’s M.O. aims at unlocking the potential of modern computer vision solutions with leading-edge hardware acceleration, engineered for user simplicity so users can design and innovate more freely. The Axelera AI platform – purpose-built using proprietary in-memory computing tech and RISC-V dataflow architecture – delivers strong performance and usability “at a mere fraction of the cost and energy consumption of other solutions,” as the company defines. (Specific Axelera milestones can be perused here.)

Recently, Axelera made a big splash; that’s what we’re covering today:

Axelera AI has successfully closed an oversubscribed $68 million Series B funding round, bringing the total amount it has raised to $120 million.

That’s a lot of AI green, to be sure.

According to the official announcement, this funding round “represents Europe’s largest oversubscribed Series B funding round in the fabless semiconductor industry, and is backed by major institutional investors like Invest-NL Deep Tech Fund, Samsung Catalyst Fund, and the European Innovation Council Fund.” This new capital reflects Axelera’s progress in building its team of more than 180 members (including 55 PhDs with more than 40,000 total citations), and it’s also indicative of the success of the company’s Metis AI Platform; that’s Axelera’s easy-to-use, non-budget-breaking solution that further simplifies development, integration and deployment of computer vision inference and AI acceleration. (Additionally, the announcement notes how Axelera’s in-progress growth strategy is “eyeing opportunities across new geographies and market sectors,” reporting how this capital will allow the team to expand into verticals like automotive, digital healthcare, Industry 4.0 and more.)

“There’s no denying that the AI industry has the potential to transform a multitude of sectors,” said Fabrizio Del Maffeo, CEO and co-founder of Axelera AI. “However, to truly harness the value of AI, organizations need a solution that delivers high-performance and efficiency while balancing cost. This funding supports our mission to democratize access to artificial intelligence, from the edge to the cloud. By expanding our product lines beyond the edge computing market, we are able to address industry challenges in AI inference and support current and future AI processing needs with our scalable, proven technology.”

Any interested in Axelera AI evaluation kits can explore options here.


Edited by Greg Tavarez
Get stories like this delivered straight to your inbox. [Free eNews Subscription]
SHARE THIS ARTICLE
Related Articles

VoIP Provider Zadarma Integrates Three AI Voice Agents into its PBX Platform

By: Erik Linask    6/11/2025

London-based VoIP provider Zadarma integrated three AI-powered voice assistants directly into its PBX platform, a first in Europe, according to the co…

Read More

The Future of CX: Mosaicx Unveils AI-Native Engage Platform

By: Erik Linask    6/6/2025

Mosaicx has launched Engage, its next-gen AI-native CX platform to drive improvements in customer engagement and experiences.

Read More

Jabra Reviving Human Focus Amid AI Revolution in Customer Experience

By: Erik Linask    5/27/2025

Jabra looks to redefine how customer service teams make good on the promise of quality CX by combining the "what" of customer conversations, with "how…

Read More

When AI Ambitions are Dictated by Cloud Matters

By: Special Guest    5/27/2025

How are increasing AI workloads changing what we know about and how we design cloud architectures?

Read More

Rising AI-Driven Infrastructure Costs Expose Critical Weaknesses: NVMe SSDs & CXL Modules Redefine Scalability

By: Special Guest    5/7/2025

AI workloads are too demanding for their existing IT architecture. GPUs remain under-utilized, not because of faulty hardware, but because data can't …

Read More

-->