Artificial intelligence and machine learning (AI/ML) are exciting technologies making huge promises, but we're collectively reaching the limit of our capabilities to achieve them. As a whole, our AI goals and ML ambitions are fast approaching the boundaries of what's actually possible. If there's going to be a future in AI and ML, neural processing units (NPUs) are the key.
For organizations that are serious about AI workloads at scale, understanding what an NPU is, how it functions, and what it’s capable of will help you make the right decision about how to build your pipelines. The right storage solution will be critical since most can’t keep up with the speed that NPUs provide.
What Is a Neural Processing Unit?
A neural processing unit is a specialized piece of hardware that is designed with a focus on accelerating neural network computations. Thanks to their design, NPUs drastically enhance the speed and efficiency of AI systems.
Don't mistake NPUs for an upgraded piece of familiar tech: NPUs are a huge leap forward for AI/ML processing. Optimized for running the algorithms that make AI and ML possible, NPUs are particularly efficient at tasks like image recognition and natural language processing, which require fast processing of massive amounts of multimedia data.
NPUs don't necessarily compete with their more recognizable counterparts, CPUs (central processing units) and GPUs (graphics processing units). Instead, NPUs are complementary to them and their roles.
CPUs, even the very best ones, are still only general-purpose computing engines. CPUs are capable of handling a broad range of tasks but lack specialized optimization for a number of tasks. GPUs, on the other hand, are specialized for parallel processing, and they're particularly good at complex computations in graphics. Thanks to digital currency mining, GPUs have developed a reputation for processing machine learning workloads but need special circuits to be especially effective at such tasks.
How Does a Neural Processing Unit Work?
NPUs are specially designed to process machine learning algorithms. While GPUs are very good at processing parallel data, NPUs are purpose-built for the computations necessary to run neural networks responsible for AI/ML processes.
Machine learning algorithms are the foundation and scaffolding upon which AI applications get built. As neural networks and machine learning computations have become increasingly complex, the need for a custom solution has emerged.
NPUs accelerate deep learning algorithms by natively executing many of the specific operations neural networks need. Rather than build the framework for running those operations, or running environments that allow for those advanced computations, NPUs are custom-built to execute AI/ML operations efficiently.
NPUs and their built-in capability for high-performance computation have drastic impacts on AI performance. Matrix multiplications and convolutions are specialized tasks AI processes depend on and NPUs excel at. Image recognition and language processing are the places NPUs are currently transforming the industry, boasting faster inference times and lower power consumption, which can impact an organization’s bottom line.
Applications of Neural Processing Units
The application of neural processing units extends to any industry or field that needs rapid, efficient, scalable processing of AI/ML workloads. NPUs are being deployed in natural language processing for sentiment analysis, language translation, text summarization, and chatbots. When used in cybersecurity, NPUs process huge amounts of data and enable threat, anomaly, and intrusion detection. NPUs are significantly better at parsing visual data and are used in autonomous vehicles and healthcare—two fields that require rapid image analysis.
The world NPUs open up to us is still largely unexplored. At the consumer level, NPUs (which are already largely integrated into smartphones and SoC) blur backgrounds in video calls and generate AI images on the fly. But the true extent of what NPUs are capable of has yet to be revealed.
Advantages and Limitations of Neural Processing Units
NPUs provide faster inference speeds and accelerate inference tasks in deep learning models. When neural network computations are offloaded to NPUs, latency is reduced and user experience can be improved. NPUs are increasingly deployed in edge and IoT devices thanks to how much more power efficient they are than their GPU and CPU counterparts.
But NPUs have a downside: They can be too fast. Data storage systems comprised of data lakes and data warehouses were developed in response to the hard, physical limitations of data processing speeds. The speed of NPUs can overwhelm traditional storage systems.
To be properly used at scale, NPUs need a holistic storage solution that's fast enough to keep up. At the enterprise level, storage has to be purpose-built for AI. Take, for example, Pure Storage® FlashBlade//S™, which is designed to be a through-put, shared, scaled-out architecture capable of handling large-scale AI/ML pipelines.
There's also AI-ready infrastructure. Designed to turn the potential disadvantage of NPUs' blistering speeds into an asset, AIRI® is a full-stack solution that simplifies AI deployment and scales quickly and efficiently.
Neural Processing Units vs. Graphics Processing Units
As mentioned above, NPUs and GPUs differ significantly in architecture, performance, and application. NPUs and GPUs are different pieces of hardware, each optimized for what it does best: NPUs for AI/ML tasks and GPUs for graphics rendering.
Since NPUs are specialized hardware designed specifically to accelerate neural network computations, their architecture is custom-built for deep learning tasks. GPUs, in contrast, have to be repurposed for deep learning tasks and are much stronger in graphics rendering. GPUs have a generalized architecture with thousands of cores. NPUs feature a more streamlined design with dedicated hardware for tasks like matrix multiplications and convolutions.
NPUs tend to outperform GPUs in real-time inference tasks in edge devices, where low latency and energy efficiency are key. NPUs are also preferable in applications that call for on-device AI processing—think autonomous vehicles and IoT devices. And NPUs beat out GPUs for AI workload speeds in resource-constrained environments.
Conclusion
In any project, there's a constant trade-off between having the right tool for each part of the job and the simplicity of having one, generalized tool. That trade-off is why, for example, amateur woodworkers don't invest in a circular saw, a miter saw, a jigsaw, a table saw, a band saw, a rotary saw, and a chain saw until they need one for the project they're working on. Similarly, the AI/ML world was getting by just fine with GPUs until recently.
Neural processing units are powerful, custom-built tools for artificial intelligence and machine learning algorithms. NPUs could very well revolutionize the face of AI/ML workloads. And it makes sense that more networks and companies are investing in them: AI and ML are poised to reshape our culture, technologies, and even our art.
Harnessing the full power and efficiency of NPUs at scale takes reimagining what's possible on the storage side of the house. But it's not just reimagining what's possible with AI/ML, you may also have to reimagine your storage, hybrid, or cloud networks to make sure that while your NPUs are pulling in and processing huge amounts of data fast, you have a storage solution that can keep up.
