CPU vs GPU vs NPU: What's the difference?

With the advent of AI comes a new type of computer chip that's going to be used more and more. By now you've probably all heard of the CPU, the GPU, and more recently the NPU. Let's untangle the difference between these different computing units and how to best use them. But first, a history lesson.

First introduced in the 1960s, CPUs (central processing units) are the beating heart of all computers, responsible for performing all basic operations. Designed to be versatile and capable of handling a wide range of instructions and operations, they are ideal for running operating systems, productivity software, and many other general-purpose applications. However, with the advent of the first 3D video games and advanced graphics applications, the limitations of CPUs became apparent. Designed for general-purpose computing, their architecture was not optimized for the massive parallel processing required by graphics-intensive applications and scientific simulations.

However, with the growing demand for massive parallel processing required by graphics-intensive applications and scientific simulations, the limitations of CPUs and math coprocessors became apparent. This led to the development of graphics processing units (GPUs) in the 1990s, which quickly became indispensable and specialized for parallel processing of large amounts of data. GPUs (available as integrated graphics chips or stand-alone graphics cards) are built with hundreds or thousands of small, specialized cores (ALUs: Arithmetic Logic Units) that can perform multiple operations simultaneously, making them ideal for graphics rendering and, more recently, for training and deploying deep learning models.

In the last few years, a new category has emerged called Neural Processing Units (NPUs). While math coprocessors and GPUs have accelerated floating-point calculations and parallel processing of large amounts of data, NPUs are designed to efficiently handle matrix multiplication and addition, which is essential for artificial intelligence (AI) and machine learning (ML) workloads such as image recognition, natural language processing, and machine learning.

In practice, CPUs, GPUs, and NPUs are all essential to the operation of a modern computer, but each is optimized for different types of computation and rendering. Let's break it down.

At the heart of any computing device is the processor, often referred to as the "brain" of the system. It is known for its versatility and general-purpose computing capabilities, thanks to an architecture designed to manage applications and tasks that require complex decision-making.

Strengths

• Compatibility
  Virtually all software applications are designed to run on the CPU, ensuring seamless integration with existing systems.
  
• Versatility
  Whether running operating systems or executing complex algorithms, CPUs can easily handle diverse workloads.

Weaknesses

• Limited parallelism
  Traditional CPUs are limited by their inability to efficiently handle parallel tasks, creating bottlenecks in parallel computing scenarios.
  
• Scaling cost
  Implementing CPU-based computing to meet the needs of AI workloads can be prohibitively expensive, especially for large-scale deployments.
  
  

Originally designed to render graphics in video games, GPUs have changed the game in AI with their unrivaled parallel processing capabilities, such as deep learning and image processing.

Unlike CPUs, GPUs excel at performing thousands of computational tasks simultaneously in parallel, making them essential for training and operating complex neural networks.

Strengths

• Parallel processing power
  With thousands of cores optimized for parallel computing, GPUs enable increasingly realistic graphics. They also dramatically accelerate AI workloads, reducing training times from weeks to hours.
• Scalability
  By harnessing the power of multiple GPUs in parallel, organizations can seamlessly scale their AI infrastructure to meet their evolving needs.

Weaknesses

• Specific use cases
  While GPUs excel at parallel processing tasks, they may not be as efficient at sequential or single-threaded applications, limiting their versatility

In the quest for AI innovation, a new player has entered the scene: the NPU (Neural Processing Unit). Designed from the ground up to accelerate neural network computations, NPUs are tailor-made to meet the demands of deep learning and AI workloads. NPUs deliver unmatched performance and power efficiency through hardware and software optimization.

Strengths

• AI-specific optimization
  NPUs are specifically designed to accelerate the processing and training of neural networks, delivering superior performance over CPUs and GPUs.
  
• Energy Efficiency
  By minimizing unnecessary operations and maximizing computational efficiency, NPUs consume far less power than their CPU and GPU counterparts, making them ideal for battery-powered devices and IoT applications.
  
• Edge computing capabilities
  NPUs are well suited for use in edge computing environments where low latency and real-time data processing are essential.

Weaknesses

• Development complexity
  Developing and optimizing software applications for NPUs requires specialized expertise and tools, which can increase development costs and time-to-market.
  
• Limited versatility
  While NPUs excel at AI-specific tasks, they are not well suited for general-purpose computing tasks, which limits their applicability.