Key Takeaways
NPUs specialise in AI & ML duties with excessive power effectivity & parallel processing capabilities.
GPUs excel in graphic duties like picture processing & rendering however also can deal with data-intensive operations.
NPUs speed up neural processing, whereas GPUs have numerous purposes, together with coaching AI fashions & cryptocurrency mining.
With synthetic intelligence (AI) going mainstream, the neural processing unit (NPU) has change into an essential consideration when searching for a next-gen PC or laptop computer. However have you learnt the distinction between an NPU and a graphics processing unit (GPU)?
What’s an NPU?
An NPU is a specialised processor used to speed up neural community operations, together with AI and machine studying (ML) computation duties. It consists of particular {hardware} optimizations that make it extra performant whereas nonetheless attaining excessive power effectivity.
NPUs have parallel processing capabilities (in a position to execute a number of operations concurrently), and with {hardware} structure optimizations, they’ll effectively carry out AI and ML duties like inference and coaching. NPUs can be utilized to carry out completely different AI duties, like facial recognition, and even practice AI programs.
If you would like to know extra, try our article on what an NPU is and how one can decipher its specs.
What’s a GPU?
A GPU is a particular processor used to speed up graphic duties like picture/video processing and rendering. Much like NPUs, GPUs assist parallel processing and may carry out trillions of operations per second.
Initially used for accelerating graphics processing and rendering duties like picture/video enhancing and gaming, GPUs at the moment are used for a variety of computational duties. Attributable to their excessive throughput, GPUs carry out data-intensive operations like large-scale information processing and sophisticated calculations like cryptocurrency mining.
For a similar purpose, GPUs are additionally used to coach giant neural networks. For instance, tech corporations use Nvidia’s enterprise-grade H-100 GPUs to coach their giant language fashions (LLM). Our GPU explainer dives deep into what a GPU is and the way it works.
NPU vs. GPU Comparability
The important distinction between an NPU and a GPU is that the previous accelerates AI and ML workloads whereas the latter accelerates graphic processing and rendering duties. In different phrases, every is a specialised processor for a particular perform in your machine.
On prime of their specialised perform, GPUs are additionally more and more utilized in different common computational duties, together with coaching AI programs and deep studying inference. But when a GPU may also be used for AI/ML duties, why do corporations hassle to have a devoted processor for that? The brief reply is efficiency and effectivity.
Utilizing a devoted processor in computer systems for a particular job (sometimes to speed up the duty’s efficiency) is known as {hardware} acceleration. It helps enhance efficiency as a result of completely different elements are designed to carry out particular duties extra effectively than utilizing a general-purpose part like a CPU for every part.
Because of this, {hardware} acceleration is fairly commonplace on fashionable PCs. For instance, you will discover a GPU for graphics processing and a sound card for audio.
Each GPU and NPU efficiency is measured by way of what number of trillions of operations the processor can carry out per second. That is normally denoted by Tera (or Trillion) Operations Per Second (TOPS). For example, Qualcomm’s Snapdragon X Elite chips boast as much as 45 TOPS (from the NPU alone), whereas NVIDIA’s GeForce RTX 4090 GPU has over 1300 TOPS.
GPUs might be discrete (separate from the CPU) or built-in (constructed into the CPU). As of writing, NPUs are built-in into the CPU. For instance, Apple’s A-Sequence and M-Sequence processors have an NPU (the so-called Apple Neural Engine) constructed into the CPU. Nevertheless, some NPUs are discrete, just like the Raspberry Pi’s official NPU hat.
In conclusion, an NPU is a processor that accelerates neural processing, whereas a GPU is a specialised processor for graphics processing. Attributable to their parallel processing structure, each can carry out trillions of operations per second.
Whereas NPUs are solely specialised for AI and ML duties, GPU use circumstances have expanded in recent times past graphics. They’re additionally utilized in different general-purpose purposes, particularly in data-intensive operations like coaching AI fashions and mining cryptocurrencies.
