EdgeCortix Secures $100M in Series B Funding to Revolutionize AI at the Edge

August 18, 2025
EdgeCortix Secures $100M in Series B Funding to Revolutionize AI at the Edge
  • Approximately $50 million of the recent funding includes government project awards that support the company's expansion and innovation efforts.

  • CEO Dr. Sakyasingha Dasgupta stated that the funds will accelerate the deployment of AI co-processors like the SAKURA-II and the next-generation SAKURA-X chiplet platform, capable of delivering up to 2,000 TOPS with low power consumption.

  • Major global investors such as Yanmar Ventures, Pacific Bays Capital, NTT Finance, SiC Power, Aero X Ventures, SBI Investments, and GHOVC participated in the Series B round, demonstrating strong confidence in the company's technology and market potential.

  • Founded in 2019 and headquartered in Tokyo, EdgeCortix develops silicon-based, energy-efficient AI processors designed for generative AI workloads at the edge, serving industries including defense, aerospace, smart cities, and telecommunications.

  • EdgeCortix Inc., a Tokyo-based fabless semiconductor company specializing in energy-efficient AI processing at the edge, announced the initial close of its Series B funding round, bringing total funding since December 2024 to nearly $100 million USD, including recent government awards.

  • Investors expressed strong confidence in EdgeCortix's innovative technology and market strategy, highlighting its potential to grow across sectors such as robotics, industrial automation, defense, aerospace, and space exploration.

  • The company's AI chips are gaining design wins in various industries, with recent recognition from the U.S. Defense Innovation Unit emphasizing its dual-use capabilities.

  • The Series B funding round remains open, with additional closings expected throughout 2025 due to ongoing global investor interest, supporting the company's rapid growth and international expansion.

  • This funding will enable EdgeCortix to enhance production capabilities and expand its impact in high-performance physical AI systems.

Summary based on 3 sources


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