REPORT: What Makes the NPU Unique

May 20, 2024 / Ben Bajarin and Max Weinbach

Table of Contents

What Makes the NPU Unique

Now that AI is front and center, the NPU has become more important than ever for computing devices.  But what differentiates the NPU and why is it better suited than CPUs and GPUs for many AI workloads? When looking specifically at the NPU’s role, it is its dedicated role on AI workloads we think can’t be overstated. While the CPU and GPU have capabilities to run AI workloads, they also have a myriad of other jobs and functions. Running AI workloads on the CPU and GPU will disadvantage other system workloads that run exclusively on those blocks. The result could be drastic performance latency and sluggishness of the system. The NPU’s one job is to run AI workloads and doing so will free up the CPU and GPUs for their primary jobs as well. 

There are other, very specific architectural reasons to run AI workloads on the NPU as well. Below are a number of key architectural advantages the NPU has over the other core blocks on the SoC. 

Specialization for Neural Networks: Unlike general-purpose Central Processing Units (CPUs) and Graphics Processing Units (GPUs), NPUs are specifically tailored for the fundamental operations of neural networks. These operations include tensor operations, matrix multiplications, and activation functions. By optimizing their architecture for these specific tasks, NPUs can execute AI and ML algorithms with unparalleled efficiency.

Massive Parallelism: One of the hallmarks of NPUs is their ability to handle massive parallel processing tasks. With the capability to execute thousands of parallel tensor operations simultaneously, NPUs can process the vast amounts of data involved in neural networks and deep learning applications at breakneck speeds. This parallel processing prowess significantly accelerates AI computations, enabling faster inference times.

Power Efficiency: Power efficiency is a critical consideration, especially in mobile devices and edge computing applications where battery life is limited. NPUs excel in this aspect due to their specialized design. They can perform the required computations for AI applications while consuming less power compared to CPUs and GPUs. For many AI tasks the NPU consumes milliwatts of power instead of watts. This power efficiency makes NPUs an ideal choice for resource-constrained environments.

Optimized Data Flow and Memory Access: NPUs often incorporate novel dataflow architectures or in-memory computing capabilities to optimize data management and processing. By efficiently handling the flow of data through the processor, NPUs can reduce latency and increase throughput for AI workloads. This optimization is crucial for real-time AI applications where quick response times are essential.

Low-Precision Arithmetic: AI and ML applications can often tolerate lower precision arithmetic without compromising overall accuracy. NPUs leverage this characteristic by focusing on low-precision arithmetic. By performing more operations per cycle using lower precision, NPUs further enhance their computational efficiency for AI tasks. This approach allows for faster processing while maintaining acceptable accuracy levels.

Scalability: As AI models continue to grow in complexity and size, scalability becomes a critical factor. NPUs are designed with scalability in mind, allowing for the creation of larger and more powerful AI models and applications. This scalability ensures that NPUs can keep pace with the ever-increasing demands of AI workloads.

NPU Cores: Tailored for AI Acceleration: NPUs represent a shift in computing architecture, specifically designed to accelerate neural network processing. They adopt a “data-driven parallel computing” approach, optimizing for the types of parallel computations that dominate AI workloads, such as tensor operations and activations. NPUs often incorporate specialized cores, such as scalar, vector, and matrix cores, to efficiently handle different aspects of AI computations. More often than not, an NPU architecture will have a large cluster of matrix cores, but ultimately these decisions are up to the designer. 

NPUs also prioritize keeping workloads in closed loops, minimizing data movement and reducing latency. They feature optimized memory access patterns and efficient data flow, ensuring that data is readily available for computation. Additionally, NPUs are designed to be flexible and programmable, allowing developers to customize and optimize their AI models and algorithms for specific hardware configurations.

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