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Syntiant Brings Real-Time Sensor AI to Avnet Technology Showcase

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Company’s Neural Decision Processors Deliver Always-On, Low-Power Intelligence at the Edge for Faster, More Efficient Decision-Making

Syntiant Corp., the recognized leader in low-power edge AI deployment, announced that it will present “Advanced Signal Processing: Leveraging NPUs for Real-Time Sensor Data” during the Avnet Technology Showcase on Tuesday, September 9, held at the DoubleTree by Hilton Hotel Bloomington in Minneapolis, Minn.

The Avnet Technology Showcase is a free, full-day event offering more than 20 technical training courses, a trade show with over 50 suppliers, and networking opportunities for engineers and supply chain professionals.

Scheduled from 9 to 10 a.m., Syntiant’s session will demonstrate how to bring real-time sensor AI to the edge using the company’s purpose-built Neural Decision Processors™ (NDPs) and tools from Edge Impulse. Participants will be guided through the complete machine learning pipeline, from data acquisition and signal conditioning to model training, optimization and deployment on edge hardware. The workshop will also cover real-time processing of sensor signals, from audio to motion, to enable smarter, faster decisions at the edge for applications in wearables, smart homes, IoT and commercial markets.

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“With the explosion of connected devices, the ability to instantly process sensor data at the edge is becoming essential for performance, privacy and energy efficiency,” said Kurt Busch, CEO of Syntiant. “Our Neural Decision Processors deliver the advanced processing capabilities needed for feature-rich applications running on space- and power-constrained devices, while consuming only microwatts of energy, opening new possibilities for product designers across multiple industries.”

Syntiant’s NDPs have been independently verified to be 100x more power efficient and offer 10x the throughput when compared to existing low-power MCUs. With Syntiant’s technology, many machine learning applications, which previously could only be implemented in cloud servers or high-powered processors, can now run in a low-power, always-on domain at the edge.

Also Read: Developing Autonomous Security Agents Using Computer Vision and Generative AI

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