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EdgeCortix Raises Nearly $100M to Push AI to the Edge

  • Writer: Niv Nissenson
    Niv Nissenson
  • 7 days ago
  • 2 min read
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Tokyo-based semiconductor startup EdgeCortix has closed the first tranche of its Series B financing, bringing its total funding to nearly $100 million USD According to a press release. The round drew backing from investors including Yanmar Ventures, Pacific Bays Capital, NTT Finance Corporation, SiC Power, and Aero X Ventures — along with existing supporters such as SBI Investments and GHOVC.


The company, founded in 2019, specializes in energy-efficient AI processors designed for the “edge” — meaning devices and systems that run AI locally, closer to where data is generated, rather than in remote cloud data centers.


Why Edge Computing Matters

For years, AI breakthroughs have been framed in terms of cloud computing: powerful servers in distant data centers crunching vast amounts of data. But cloud has limits:

  • Latency — Sending data back and forth introduces delays. In autonomous driving or emergency medicine, even milliseconds can be too long.

  • Connectivity — Cloud AI depends on stable internet access. In disaster zones, remote areas, or secure environments, that’s not always possible.

  • Security & Privacy — Streaming sensitive data to the cloud can create vulnerabilities; local edge AI allows processing to stay on-device.


That’s where edge computing comes in. By embedding specialized chips like EdgeCortix’s SAKURA line directly into vehicles, drones, or cameras, AI systems can process data instantly and independently. This makes edge AI critical for:

  • Autonomous vehicles — A car can’t wait for a cloud server to decide whether to brake.

  • Defense and aerospace — Secure, mission-critical environments often restrict cloud access.

  • Emergency response — From disaster zones to hospitals, AI at the edge enables continued operation even when networks fail.

Still, edge AI has important limitations. Its computing power can’t match the scale of cloud clusters, meaning highly complex AI workloads will remain cloud-bound. And while edge devices can run offline, some use cases will always require connectivity for updates, coordination, and large-scale processing.


Bottom line: Cloud enables scale, edge ensures resilience — and the future of AI will depend on striking the right balance between the two. EdgeCortix chips — including the SAKURA-II AI co-processors and the upcoming SAKURA-X chiplet platform— are designed to deliver massive performance (up to 2,000 TOPS per device) while maintaining low power consumption. This performance-per-watt advantage is key for devices that must operate continuously without the energy budget of a data center according to Dr. Sakyasingha Dasgupta Founder and CEO of EdgeCortix. Investor Vote of Confidence

Backers emphasized the dual-use nature of EdgeCortix’s technology — serving both commercial and defense markets. Investors noted strong traction in defense and aerospace contracts, including a recent project with the U.S. Defense Innovation Unit.


“EdgeCortix is uniquely positioned through the dual-use nature of its technology, underscored by its recent high-profile project award from the U.S. Defense Innovation Unit,” said Maxwell Imai Weiss, Partner, Pacific Bays Capital.


Cloud computing has dominated the AI conversation, but the future will be hybrid. Cloud remains essential for training massive models — but inference, especially in safety-critical or offline environments, will increasingly shift to the edge.


EdgeCortix’s nearly $100M raise is less about chasing bigger LLMs in the cloud and more about enabling AI in the physical world: vehicles, factories, satellites, and battlefields.


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