Neural Processing Unit

Context

The M4 chip introduced by Apple in the iPad Pro includes a 16-core Neural Engine, referred to by Apple as the Neural Processing Unit (NPU).

About Neural Processing Unit (NPU)

• A neural processing unit, is a dedicated processor designed specifically for accelerating neural network processes. 
• Neural Network: It is a type of machine learning algorithm that mimics the human brain for processing data. 
• Capability to Handle AI-Related Tasks: Thus, the NPU can handle machine learning operations that form the basis for AI-related tasks, such as speech recognition, natural language processing, photo or video editing processes like object detection, and more.
• NPU Integration in Consumer Devices and Data Centers:  In most consumer-facing gadgets such as smartphones, laptops and tablets, the NPU is integrated within the main processor, adopting a System-on-Chip (SoC) configuration. 
  • However, for data centres, the NPU might be an entirely discrete processor, separate from any other processing unit such as the central processing unit (CPU) or the Graphics processing unit (GPU).

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How is Neural Processing Unitdifferent from CPU and GPU?

• CPUs: They  employ a sequential computing method, issuing one instruction at a time, with subsequent instructions awaiting the completion of their predecessors. 
  • In contrast, NPU harnesses parallel computing to simultaneously execute numerous calculations. This parallel computing approach results in swifter and more efficient processing. 
  • Thus, CPUs are good at sequential computing, executing one process at a time, but running AI tasks requires the processor to execute multiple calculations and processes simultaneously.
• Graphic Processing Units(GPUs): These processors possess parallel computing capabilities and incorporate integrated circuits designed to execute AI workloads alongside other tasks like graphic rendering and resolution upscaling. 
• • NPUs replicate these circuits solely to handle machine learning operations. This dedicated functionality leads to more efficient AI workload processing with reduced power consumption.
• GPUs are still used in the initial development and refinement of AI algorithms, while NPUs execute those refined language models on the consumer’s device at a later stage.

Neural Processing Unit and on-device AI

• Transition to Smaller Language Models: Large language models (LLMs) are often too large to be run on-device, leading service providers to offload processing to the cloud to deliver AI features based on their language models. 
  • However, major technology companies are now introducing smaller language models like Google’s Gemma, Microsoft’s Phi-3, and Apple’s OpenELM. 
• Emergence of On-Device AI Models: This shift towards scaled-down AI models capable of running entirely on-device is gaining traction. 
  • As on-device AI models become increasingly prominent, the role of NPUs becomes crucial, as they are responsible for deploying AI-powered applications directly on the hardware.