Untether AI Announces Collaboration with Arm To Deliver High Performance, Energy-Efficient Solutions – Yahoo Finance

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The automotive collaboration addresses the transformative era of the software-defined vehicle

TORONTO, April 16, 2024–(BUSINESS WIRE)–Untether AI, the leader in energy-centric AI inference acceleration, today announced a collaboration with Arm to develop best-in-class solutions optimized for next-generation automotive applications. As part of this collaboration, Untether AI will enable its inference acceleration technology to be implemented alongside the latest-generation Automotive Enhanced (AE) technology from Arm, to create solutions that address performance, power, safety, and security requirements for advanced Driver Assistance Systems (ADAS) and autonomous vehicle (AV) applications.

Compute workloads radically changed in the past several years with the deployment of AI and the increasing complexity of AI and conventional software algorithms has elevated the demand for heterogeneous computing platforms employing domain-specific architectures. This trend, which is especially true in the automotive sector, results in an ideal computing platform that combines a mix of CPUs, AI accelerators, memory, and networking technologies that have been optimized for neural network acceleration and software-defined vehicles (SDV).

“The increase in advanced user experiences, ADAS, and AV applications means more heterogeneous compute is needed in the vehicle,” said Suraj Gajendra, vice president of products and solutions, Automotive Line of Business, Arm. “By collaborating with Untether AI to ensure our leading-edge AE technology can work well alongside Untether AI’s acceleration capability, we are enabling our partners to power cutting edge AI workloads across the vehicle.”

Untether AI’s innovative at-memory architecture for neural network inference delivers the high performance and energy efficiency needed to support next generation SDV technology. High AI compute performance with low latency in a small form factor and low-power envelope have become the cornerstone requirements for leading ADAS platforms at L2+ levels and above. This need for high levels of AI compute performance in a small footprint has been further accelerated by the general trend toward an underlying centralized architecture as the automotive industry embraces software-defined vehicles (SDV).

Through close collaboration with leading OEMs, automotive suppliers, and membership in the SOAFEE consortium, Untether AI has developed an in-depth understanding of the unique requirements associated with emerging SDV architectures. SOAFEE, an industry-wide consortium, is focused on developing a common SDV platform enabling lower cost, and faster time-to-market deployment.

“Designed expressly for the automotive market, Untether AI’s chiplet-based solution delivers unsurpassed energy efficiency without compromising performance and meeting stringent safety requirements,” said Robert Beachler, vice president of product, Untether AI. “This capability, combined with Arm’s AE IP portfolio enables the flexibility, scale and choice OEMs need to create differentiating next generation intelligent automotive applications.”

About Untether AI

Untether AI® provides energy-centric AI inference acceleration from the edge to the cloud, supporting any type of neural network model. With its at-memory compute architecture, Untether AI has solved the data movement bottleneck that costs energy and performance in traditional CPUs and GPUs, resulting in high-performance, low-latency neural network inference acceleration without sacrificing accuracy. Untether AI embodies its technology in runAI® and speedAI® devices, tsunAImi® acceleration cards, and its imAIgine® Software Development Kit. Founded in Toronto in 2018, Untether AI is funded by CPPIB, GM Ventures, Intel Capital, Radical Ventures, and Tracker Capital. More information can be found at www.untether.ai.

All references to Untether AI trademarks are the property of Untether AI. All other trademarks mentioned herein are the property of their respective owners.

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