Kinara Forms Collaborations with Ampere and Mirasys to Drive Widespread Adoption of AI Solutions

Ecosystem expansion aims to create open, efficient, and more cost-effective AI platforms

Kinara, the developers of AI processors for edge computing applications and Generative AI, announced complementary collaborations with Ampere and Mirasys. Ampere, with its Cloud Native Processors that deliver increased performance for cloud computing applications, will collaborate with Kinara as part of the recently formed AI Platform Alliance to validate joint AI solutions that provide a better alternative than the GPU-based status quo and help make AI platforms more open and transparent. Separately, advanced video AI analytics developer, Mirasys (India), has partnered with Kinara to provide real-time edge-based video analytics solutions in the smart retail and banking sectors.

Kinara’s collaboration with Ampere relates to the formation of a new consortium called the AI Platform Alliance. Led by Ampere, this consortium is initially comprised of Kinara and eight other companies, with additional participating members in the coming months. The primary goals of this consortium are to reduce system complexity and promote better collaboration and openness with AI solutions and to increase the power efficiency and cost efficiency of hardware for AI to deliver better total performance than with GPUs.

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Expanding on the focus of the AI Platform Alliance, Ampere’s Chief Product Officer Jeff Wittich said, “We look forward to working with Kinara and our other consortium members to validate joint AI solutions that provide a better alternative than the GPU-based status quo. By developing these solutions as a community, we will accelerate the pace of AI innovation by making platforms more open and transparent, as well as by increasing the efficiency of AI to solve real-world problems and delivering sustainable infrastructure at scale.”

Within the Kinara and Mirasys partnership, Mirasys has seamlessly integrated Kinara’s Ara-1 Edge AI processors into its embedded platforms and cutting-edge analytics applications. This merging of technology provides a powerful solution for industries to perform real-time video analytics directly at the edge. This partnership opens a plethora of opportunities and provides customers an opportunity to use AI with low total cost of ownership and fast return on investment.

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Customers can expect enhanced performance, reliability, and security in their video surveillance systems. By processing video data at the edge, this partnership also yields reduced bandwidth and storage costs, optimizing resource utilization. Moreover, the integration of advanced analytics and insights derived from video data through AI and machine learning empowers businesses with valuable data-driven decision-making tools. Finally, the partnership is flexible and adaptable, offering customized solutions tailored to use cases that include smart city initiatives, traffic management, retail, banking, and hospitality.

Elaborating further about the partnership, Mirasys (India) MD Arindam Das Sarkar said, “We are thrilled to announce our partnership with Kinara, aiming to deliver innovative AI solutions to our customers. This collaboration leverages our strengths in AI and edge computing, allowing customers to access AI benefits without the need for extensive infrastructure investments, ensuring sustainable AI goals. Exciting times lie ahead for our joint endeavors.”

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Kinara’s CEO Ravi Annavajjhala echoed the sentiments saying, “Our involvement in the AI Platform Alliance will help demonstrate the power and cost effectiveness of using our dedicated Edge AI processor and make it easier for customers to adopt more open AI solutions. Furthermore, our partnership with Mirasys opens up exciting possibilities for edge-based video analytics in what has become a very dynamic market. By combining Kinara’s high-performance Ara-1 chipset with Mirasys’ innovative applications, businesses of all sizes can leverage the power of AI for enhanced safety, security and operational efficiency.”

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