Series C funding round co-led by Celesta Capital and GreatPoint Ventures will drive AI inference system delivering 10x higher-compute density and power efficiency
Recogni, Inc., the leader in AI-based computing, announced the closing of $102 million in Series C funding, co-led by Celesta Capital and GreatPoint Ventures. Existing investors Mayfield, DNS Capital, BMW i Ventures, and SW Mobility Fund also participated. Joining the round are new investors Pledge Ventures and Tasaru Mobility Investments, a company wholly owned by Public Investment Fund (PIF), with debt provided by HSBC Innovation Banking. The funding will be used to drive next generation system development for AI inference solutions that boost performance and power efficiency while providing the lowest total cost of ownership.
Today’s AI innovators face challenges that restrict AI’s widespread use across various markets. With AI technology advancing rapidly and model sizes increasing significantly, AI inference—the process of using live data in trained models for predictions or task solutions—needs to be more performant and power-efficient than past solutions. These improvements are crucial for broadening AI application in a sustainable manner. Recogni’s scalable, power-efficient AI inference acceleration technique promises to unlock new AI computing possibilities in critical areas like generative AI and intelligent autonomy.
Marc Bolitho, CEO of Recogni, points out the urgent gap between rapidly expanding AI models and the slower evolution of computing capability. “The critical need for solutions that directly address the key challenges in AI inference processing—compute capability, scalability, accuracy, and energy savings—is more urgent than ever. Recogni is leading this transformative wave, engineering pivotal advances that will redefine data centers and enterprise and revolutionize industries like automotive and aerospace,” he said.
Current AI acceleration solutions will not scale for tomorrow’s AI needs. Cloud-based AI training and inference predominantly utilize power-intensive GPUs (graphics processing units), exerting substantial stress on the compute capacity, cooling, and power systems of data centers. Such demands render the process financially and ecologically unsustainable. Recogni is offering a fundamental change in inference processing to solve the AI compute issue from the ground up with scalability that can stand the test of time.
“When you have a solution that achieves 10x higher compute density, 10x lower power and 13x less cost per query, it’s a no-brainer to invest and help bring that solution to market,” said Ashok Krishnamurthi, managing partner at GreatPoint Ventures. “The compute demand for AI applications is going to be significantly more than what the experts are forecasting, and addressing the power consumption piece of the puzzle is critical at this stage resulting in significantly lower operational costs. The repeat investment and high caliber of new investors in the C round is a true testament to Recogni’s team and technology.”
“Generative AI systems in the market today are highly inefficient and consume too much power while adding system complexity, but Recogni’s proven technology is raising the bar on power performance to address the large compute needs of AI workloads,” said Sriram Viswanathan, Celesta Capital founding managing partner and chairman of Recogni’s Board of Directors. “With the rapid AI proliferation over the past year, there is a clear need for Recogni’s products, which are purpose-built for efficient inference compute. We’re pleased to support the company’s ability to meet market demand with its innovative solutions and look forward to seeing them continue to expand their exciting product offering.”
Recogni introduced Recogni Scorpio, its first low power AI compute product, in December 2022. Scorpio is the world’s first 1000 TFLOPS (1 Peta-Flop) class inference solution at the lowest power for various inference applications. Scorpio’s performance validates Recogni‘s vision and ability in breaking barriers in AI compute with scalable performance, generation over generation.
SOURCE: PRNewswire
