Largest early-stage AI silicon round led by new investor Tracker Capital and co-led by existing investor Intel Capital
New funding to accelerate deployment of company’s at-memory compute technology across multiple markets
Untether AITM, the leader in at-memory computation and AI inference acceleration, announced an oversubscribed $125 million funding round led by an affiliate of Tracker Capital Management, LLC (“Tracker Capital”), a stage-agnostic venture capital investor that principally invests in high-potential technology growth companies. Tracker Capital was founded by Stephen A. Feinberg who is also Co-Founder and Co-CEO of Cerberus Capital Management. The round was co-led by Intel Capital, Intel Corporation’s global investment organization, and included participation from new investor Canada Pension Plan Investment Board (“CPP Investments”) and existing investor Radical Ventures.
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“I am pleased to add Tracker Capital to our prestigious group of investors and welcome Shaygan to our Board”
As part of the funding round, Tracker Capital Senior Advisor Dr. Shaygan Kheradpir will join Untether AI’s Board of Directors. Previously, Dr. Kheradpir served as Verizon’s Group CIO, Barclays Bank Group COO, and CEO of Coriant and Juniper Networks.
“I am pleased to add Tracker Capital to our prestigious group of investors and welcome Shaygan to our Board,” said Arun Iyengar, CEO, Untether AI. “Tracker Capital’s unmatched experience and relationships across sectors will help speed our engagements in multiple high-value markets, including telecom, technology, financial services, retail, and defense. I am also thrilled to welcome CPP Investments to the Untether AI family. With the new funding round and partnerships, we will be able to expand our current product reach and accelerate the development of our next generation products.”
Dr. Kheradpir commented on the investment from Tracker Capital: “Untether AI has a scalable architecture that provides a revolutionary approach to AI inference acceleration. Its industry-leading power efficiency can deliver the compute density and flexibility required for current and future AI workloads in the cloud, for edge computing, and embedded devices.”
In addition to new investors Tracker Capital and CPP Investments, all existing investors participated in the round, which further validates the benefits of at-memory compute and the progress made by Untether AI.
Saf Yeboah-Amankwah, Intel Chief Strategy Officer added: “We have been an investor in Untether AI since the seed round. During that time Untether AI has assembled a world-class management team, developed and launched an exceptional product, and is now poised for growth in the burgeoning AI inference acceleration space.”
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“AI has become a key enabling technology for many industries and Untether AI’s novel compute architecture has the potential to accelerate adoption across a number of use cases providing a strong value proposition to its customers,” said Leon Pedersen, Managing Director, Head of Thematic Investing, CPP Investments. “We seek to invest in innovative companies with strong market opportunities and we are pleased to support this leading Canadian AI technology company.”
Architecting the Next Generation of AI Compute
The dramatic increase in the usage of AI, along with its heavy computational requirements, is overwhelming traditional compute architectures, and drastically increasing power consumption in datacenters. Untether AI’s at-memory compute architecture breaks through the computational bottleneck and changes the paradigm for AI compute efficiency. Untether AI’s tsunAImi accelerator cards powered by runAI devices provide record-breaking energy efficiency and compute density for inference acceleration. The new funding enables the company to extend its leadership position, enhance its software offering, and build its next generation products.
Expanding Engagements in Multiple Markets
With this latest funding round, Untether AI will be well-positioned to accelerate and expand its customer engagements across a multitude of markets. Since at-memory computation is both general purpose and extremely energy efficient, it can be used in a variety of industries and applications, including banking and financial services; natural language processing; autonomous vehicles; smart city and retail; and other applications that require high-throughput and low-latency AI acceleration.
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