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The NMP-750 serves as a high-performance accelerator IP for edge computing solutions across various sectors, including automotive, smart cities, and telecommunications. It supports sophisticated applications such as mobility control, factory automation, and energy management, making it a versatile choice for complex computational tasks. With a high throughput of up to 16 TOPS and a memory capacity scaling up to 16 MB, this IP ensures substantial computing power for edge devices. It is configured with a RISC-V or Arm Cortex-R/A 32-bit CPU and incorporates multiple AXI4 interfaces, optimizing data exchanges between Host, CPU, and peripherals. Optimized for edge environments, the NMP-750 enhances spectral efficiency and supports multi-camera stream processing, paving the way for innovation in smart infrastructure management. Its scalable architecture and energy-efficient design make it an ideal component for next-generation smart technologies.
The NMP-350 is designed to offer exceptional efficiency in AI processing, specifically targeting endpoint accelerations. This IP is well-suited for markets that require minimal power consumption and cost-effectiveness, such as automotive, AIoT/Sensors, Industry 4.0, smart appliances, and wearables. It enables a wide variety of applications, including driver authentication, digital mirrors, machine automation, and health monitoring. Technically, it delivers up to 1 TOPS and supports up to 1 MB local memory. The architecture is based on the RISC-V or Arm Cortex-M 32-bit CPU, ensuring effective processing capabilities for diverse tasks. Communication is managed via three AXI4 interfaces, each 128 bits wide, to handle Host, CPU, and Data interactions efficiently. The NMP-350 provides a robust foundation for developing advanced AI applications at the edge. Designed for ultimate flexibility, it aids in predictive maintenance and personalization processes in smart environments. With its streamlined architecture, it provides unmatched performance for embedded solutions, enabling seamless integration into existing hardware ecosystems.
NeuroMosAIc Studio is a comprehensive software platform designed to enhance the capabilities of AiM Future's processor IPs. It provides a suite of powerful tools for developing, optimizing, and deploying AI models, specifically tailored for edge applications. By supporting network compression, quantization, and hardware optimization, it allows users to maximize the performance and efficiency of AI models on low-power processors. The platform boasts tools for precision analysis and adjustment, enabling developers to fine-tune models for specific hardware architectures. NeuroMosAIc Studio offers an NMP-aware training environment, aiding in optimizing models during the training phase by facilitating efficient quantization and precision setting. Through its extensive capabilities, including C code generation and runtime libraries, NeuroMosAIc Studio empowers users to accelerate AI deployment, offering robust solutions for real-time data processing. This software is instrumental in creating adaptable AI ecosystems across various sectors, fostering advancements in edge computing and intelligence.
Aimed at performance-driven environments, the NMP-550 is an efficient accelerator IP optimized for diverse markets, including automotive, mobile, AR/VR, drones, and medical devices. This IP is crucial for applications such as driver monitoring, fleet management, image and video analytics, and compliance in security systems. The NMP-550 boasts a processing power of up to 6 TOPS and integrates up to 6 MB of local memory, empowering it to handle complex tasks with ease. It runs on a RISC-V or Arm Cortex-M/A 32-bit CPU and supports multiple high-speed interfaces, specifically three AXI4, 128-bit connections that manage Host, CPU, and Data traffic. This IP is engineered for environments demanding high performance with efficient power use, addressing modern technological challenges in real-time analytics and surveillance. The NMP-550 is adept at improving system intelligence, allowing for enhanced decision-making processes in connected devices.
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