Processing Unit

Processing units, particularly neural processing units (NPUs), are central to accelerating machine learning (ML) inference, especially in resource-constrained environments like edge devices and cloud platforms. Current research emphasizes optimizing NPU architectures and algorithms for efficient parallel processing of diverse ML workloads, including large language models and convolutional neural networks, focusing on techniques like model compression, heterogeneous computing, and efficient resource allocation to minimize latency and power consumption. These advancements are crucial for enabling real-time AI applications in various sectors, from autonomous vehicles to mobile computing, by improving both performance and energy efficiency.