The Rise of Neural Processing Units: Enhancing On-Device Generative AI for Speed and Sustainability


The evolution of generative AI will not be just reshaping our interaction and experiences with computing devices, it is usually redefining the core computing as well. One in all the important thing drivers of the transformation is the necessity to operate generative AI on devices with limited computational resources. This text discusses the challenges this presents and the way neural processing units (NPUs) are emerging to resolve them. Moreover, the article introduces a number of the latest NPU processors which might be leading the way in which on this field.

Challenges of On-device Generative AI Infrastructure

Generative AI, the powerhouse behind image synthesis, text generation, and music composition, demands substantial computational resources. Conventionally, these demands have been met by leveraging the vast capabilities of cloud platforms. While effective, this approach comes with its own set of challenges for on-device generative AI, including reliance on constant web connectivity and centralized infrastructure. This dependence introduces latency, security vulnerabilities, and heightened energy consumption.

The backbone of cloud-based AI infrastructure largely relies on central processing units (CPUs) and graphic processing units (GPUs) to handle the computational demands of generative AI. Nonetheless, when applied to on-device generative AI, these processors encounter significant hurdles. CPUs are designed for general-purpose tasks and lack the specialized architecture needed for efficient and low-power execution of generative AI workloads. Their limited parallel processing capabilities lead to reduced throughput, increased latency, and better power consumption, making them less ideal for on-device AI. On the hand, while GPUs can excel in parallel processing, they’re primarily designed for graphic processing tasks. To effectively perform generative AI tasks, GPUs require specialized integrated circuits, which devour high power and generate significant heat. Furthermore, their large physical size creates obstacles for his or her use in compact, on-device applications.

The Emergence of Neural Processing Units (NPUs)

In response to the above challenges, neural processing units (NPUs) are emerging as transformative technology for implementing generative AI on devices. The architecture of NPUs is primarily inspired by the human brain’s structure and performance, particularly how neurons and synapses collaborate to process information. In NPUs, artificial neurons act as the fundamental units, mirroring biological neurons by receiving inputs, processing them, and producing outputs. These neurons are interconnected through artificial synapses, which transmit signals between neurons with various strengths that adjust in the course of the learning process. This emulates the strategy of synaptic weight changes within the brain. NPUs are organized in layers; input layers that receive raw data, hidden layers that perform intermediate processing, and output layers that generate the outcomes. This layered structure reflects the brain’s multi-stage and parallel information processing capability. As generative AI can be constructed using an analogous structure of artificial neural networks, NPUs are well-suited for managing generative AI workloads. This structural alignment reduces the necessity for specialised integrated circuits, resulting in more compact, energy-efficient, fast, and sustainable solutions.

Addressing Diverse Computational Needs of Generative AI

Generative AI encompasses a big selection of tasks, including image synthesis, text generation, and music composition, each with its own set of unique computational requirements. As an example, image synthesis heavily relies on matrix operations, while text generation involves sequential processing. To effectively cater to those diverse computational needs, neural processing units (NPUs) are sometimes integrated into System-on-Chip (SoC) technology alongside CPUs and GPUs.

Each of those processors offers distinct computational strengths. CPUs are particularly adept at sequential control and immediacy, GPUs excel in streaming parallel data, and NPUs are finely tuned for core AI operations, coping with scalar, vector, and tensor math. By leveraging a heterogeneous computing architecture, tasks will be assigned to processors based on their strengths and the demands of the particular task at hand.

NPUs, being optimized for AI workloads, can efficiently offload generative AI tasks from the primary CPU. This offloading not only ensures fast and energy-efficient operations but in addition accelerates AI inference tasks, allowing generative AI models to run more easily on the device. With NPUs handling the AI-related tasks, CPUs and GPUs are free to allocate resources to other functions, thereby enhancing overall application performance while maintaining thermal efficiency.

Real World Examples of NPUs

The advancement of NPUs is gaining momentum. Listed below are some real-world examples of NPUs:

  • Hexagon NPUs by Qualcomm is specifically designed for accelerating AI inference tasks at low power and low resource devices. It’s built to handle generative AI tasks corresponding to text generation, image synthesis, and audio processing. The Hexagon NPU is integrated into Qualcomm’s Snapdragon platforms, providing efficient execution of neural network models on devices with Qualcomm AI products.
  • Apple’s Neural Engine is a key component of the A-series and M-series chips, powering various AI-driven features corresponding to Face ID, Siri, and augmented reality (AR). The Neural Engine accelerates tasks like facial recognition for secure Face ID, natural language processing (NLP) for Siri, and enhanced object tracking and scene understanding for AR applications. It significantly enhances the performance of AI-related tasks on Apple devices, providing a seamless and efficient user experience.
  • Samsung’s NPU is a specialized processor designed for AI computation, able to handling 1000’s of computations concurrently. Integrated into the newest Samsung Exynos SoCs, which power many Samsung phones, this NPU technology enables low-power, high-speed generative AI computations. Samsung’s NPU technology can be integrated into flagship TVs, enabling AI-driven sound innovation and enhancing user experiences.
  • Huawei’s Da Vinci Architecture serves because the core of their Ascend AI processor, designed to reinforce AI computing power. The architecture leverages a high-performance 3D cube computing engine, making it powerful for AI workloads.

The Bottom Line

Generative AI is transforming our interactions with devices and redefining computing. The challenge of running generative AI on devices with limited computational resources is important, and traditional CPUs and GPUs often fall short. Neural processing units (NPUs) offer a promising solution with their specialized architecture designed to satisfy the demands of generative AI. By integrating NPUs into System-on-Chip (SoC) technology alongside CPUs and GPUs, we will utilize each processor’s strengths, resulting in faster, more efficient, and sustainable AI performance on devices. As NPUs proceed to evolve, they’re set to reinforce on-device AI capabilities, making applications more responsive and energy-efficient.


What are your thoughts on this topic?
Let us know in the comments below.

0 0 votes
Article Rating
Inline Feedbacks
View all comments

Share this article

Recent posts

Would love your thoughts, please comment.x