Nota AI and SiMa.ai
Partnership for edge AI in industry
Nota AI and SiMa.ai have agreed on a strategic collaboration to develop AI solutions for industrial applications. The aim is to optimize AI models for use directly on devices at the network edge, where high computing power and energy efficiency are required simultaneously.
The core of the cooperation is the combination of Nota AI's NetsPresso model optimization platform with SiMa.ai's MLSoC chips. The software reduces model sizes by more than 90 percent while maintaining accuracy and is designed to maximize inference performance on the hardware. At the same time, close integration of the NetsPresso SDK and Palette SDK development environments is being implemented.
The partners are planning joint pilot projects and marketing, supported by SiMa.ai's global sales channels. Fields of application include intelligent traffic systems as well as security and monitoring solutions.
In addition, the video analysis solution Nota Vision Agent is being optimized for the platform. In the future, the collaboration will be extended to other areas of physical AI, including robotics and mobility.
The hardware basis is the Modalix MLSoC from SiMa.ai, designed for multimodal AI inference with low energy consumption. In combination with software tools for the provision of complex applications, the platform is designed to enable real-time processing and scaling of AI workloads in industrial environments.
The companies for cooperation
Myung-su Chae, CEO of Nota AI, commented: "We see great significance in the fact that Nota AI's AI optimization technology combined with SiMa.ai's platform can accelerate our expansion into physical AI. We look forward to working with SiMa.ai to develop AI solutions on devices that can be practically deployed in real industrial environments."
Krishna Rangasayee, founder and CEO of SiMa.ai added: "Software optimization is not optional - it is essential for AI models to run reliably in physical AI environments. We are confident that Nota AI's AI optimization expertise, which maximizes the performance of AI models for specific hardware environments, will play an important role in the implementation of SiMa.ai's physical AI strategy.
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