Industry Expert Blogs

Senior ABI Research analyst Yih-Khai Wong recently noted that edge artificial intelligence (AI) applications and use cases are gaining popularity across many industries. Instead of transferring massive amounts of data to the cloud, edge AI significantly reduces latency by allowing cameras and sensors to rapidly—and directly—analyze petabytes of high-resolution images and videos. Edge AI systems typically require sophisticated inference capabilities enabled by complex convolutional neural networks (CNNs) and transformers. Although GPUs support CNNs and transformers, dedicated AI accelerators optimized for both models are a better option for edge applications that demand high-performance inference capabilities in the smallest area with the least power.

Read on to learn why companies use low-power, high-performance neural processing units (NPUs) such as the Synopsys ARC® NPX6 NPU IP to design dedicated edge AI processors for a wide range of applications, from surveillance anomaly detection and event-based cameras to augmented reality (AR) and virtual reality (VR). Optimized for both CNNs and transformers, the ARC NPX6 NPU IP recently received the “Best Edge AI Processor” in the 2023 Edge AI and Vision Product of the Year Awards presented by the Edge AI and Vision Alliance.

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