EdgeCortix

Tokyo 104-0061

COMPANY OVERVIEW

About EdgeCortix

EdgeCortix makes energy-efficient AI chips and software for the edge.

Contact

GINZA SIX 13F, 6-10-1, Ginza, Chuo-ku
Tokyo 104-0061
Japan
https://www.edgecortix.com/en/
+81-3-6417-9661

More Info on EdgeCortix

In July 2019, Dr. Sakyasingha Dasgupta established EdgeCortix as a fabless semiconductor company headquartered in Tokyo. Seeing the possibilities for growth in AI, the company has garnered funding from a marquee group of investors including Japan’s foremost venture capital firm SBI Investment Co. Ltd, as well as Global Hands-On VC (GHOVC), a leading Japan-US collaboration-focused VC. EdgeCortix has also received significant investment from Renesas Electronics Corporation, a long-term customer and a premier global supplier of semiconductor solutions.

With the focus on developing specialized AI accelerators for edge computing using a software-driven approach, over the past five years (and 20+ patents granted or applied for), our engineering teams in Japan developed the MERA Compiler and Software framework, along with our Dynamic Neural Accelerator (DNA) – a novel runtime-reconfigurable processor architecture. We validated this architecture and designed the initial SAKURA silicon solution based on our patented DNA architecture.

Recently, we unveiled SAKURA-II, our next-generation production silicon. SAKURA-II excels in flexibility, power efficiency, and real-time processing of complex models including Generative AI at the edge. With industry-leading memory capacity and bandwidth, SAKURA-II uses the advanced DNA architecture to seamlessly handle vision tasks and large language models (LLMs) in low-power environments.

With a global operational footprint, including Japan, India, Singapore, and the United States, we are very proud of our journey and our team’s accomplishments so far, and we recognize that this is just the beginning. We invite you to explore our website and encourage you to reach out and learn how EdgeCortix can address your unique edge inference needs.

Articles & News

Dreamstime
dreamstime_l_330310388
Machine Learning

Generative AI at the Edge: Engineering Challenges

Oct. 8, 2024
The increasing demands for generative AI at the edge call for new solutions that can achieve high performance within the low-power requirements for edge apps.

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