DEEPX, a South Korean startup specializing in on-device AI chip technology (NPU), recently announced a significant milestone in its funding journey. The company successfully raised $80 million (KRW 108.5 billion) in a Series C round, catapulting its valuation to $529 million (KRW 723 billion). This valuation marks a remarkable eight-fold increase from its Series B funding of around $15 million in 2021.
With the Series C funding, totaling approximately $95 million, DEEPX aims to gear up for mass production of its flagship products – DX-V1, DX-V3, DX-M1, and DX-H1. These products are slated for release in late 2024, targeting global distribution. Additionally, the startup plans to accelerate the development and launch of its next-generation large language model (LLM) on-device solutions.
Founded in 2018 by CEO Lokwon Kim, who brings extensive experience from renowned tech giants like Apple, Cisco Systems, and IBM, DEEPX is poised to capitalize on the burgeoning global edge AI market. This market, also known as on-device AI, is projected to exceed $107.47 billion by 2029, fueled by applications such as facial recognition, robotics, smart mobility, and IoT.
DEEPX sets itself apart in the competitive AI chip landscape with its focus on cost efficiency, energy consumption efficiency, and comprehensive AI solutions. Its product lineup caters to various applications, including vision systems for home appliances and surveillance cameras, AI computing boxes, and smart factories.
Despite not having finalized customers, DEEPX is actively collaborating with over 100 potential clients and strategic partners, including Hyundai Kia Motors Robotics Lab and Korean IT company POSCO DX. The latest investment round was led by SkyLake Equity Partners, with participation from BNW Investments, AJU IB, and previous investor Timefolio Asset Management.
DEEPX’s innovative approach to on-device AI technology positions it as a promising player in the rapidly evolving AI chip market, offering potential solutions to address energy consumption and cost challenges associated with traditional data center-based AI services.
