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Abstract
The NPU (Neural Processing Unit) is a chip designed specifically for neural network computations, excelling in matrix operations and convolution tasks, characterized by low power consumption and high efficiency. It is mainly used for inference tasks on edge devices, such as image recognition and speech processing. However, it faces performance and ecological limitations in large model training and complex computational tasks.
In the current wave of digitalization, large models, with their powerful performance and wide applications, have become a key factor driving the advancement of artificial intelligence ๐. From intelligent chat assistants in natural language processing to precise image recognition and processing in computer vision, large models demonstrate unparalleled potential, continuously expanding the boundaries of artificial intelligence. However, the stringent computational resource requirements for large model training and inference make hardware performance a core factor determining their development. Existing NPUs reveal numerous shortcomings when adapting to large model training and inference tasks, primarily focusing on four key dimensions: hardware performance, architectural design, software ecosystem, and cost-power consumption.
1. Hardware Performance Shortcomings
(1) Severe Insufficiency of Computing Power
Large model training and inference are typical compute-intensive tasks, with computing power requirements reaching astronomical figures ๐ฅ. For instance, training a model with a trillion parameters often requires hundreds, thousands, or even tens of thousands of GPUs working in collaboration. During training, the model must perform complex calculations on massive datasets, continuously adjusting parameters to optimize performance. In contrast, mainstream NPUs exhibit relatively limited computing power. The NPU in Intel’s Meteor Lake has a maximum computing power of only 10 TOPS, while AMD’s Ryzen 7000 and 8000 series NPUs hover around 12 to 16 TOPS. Such limited computing power is akin to a small horse pulling a large cart when faced with the complex computational demands of large models, making it impossible to meet the urgent need for efficient computation during training and inference, severely restricting the operational efficiency of large models.
(2) Significant Memory Bandwidth Bottleneck
Large model training and inference not only rely on powerful computing capabilities but also require handling massive parameters and data, making memory bandwidth another critical limiting factor. In actual operation, data is frequently transferred between memory and computing units, necessitating extremely high memory bandwidth. For example, NVIDIA’s H100 has a memory bandwidth of up to 3.35TB/s, efficiently supporting rapid data flow. However, NPUs typically do not come with DRAM and must rely on system RAM, which leads to limited data transfer speeds when processing large models, easily resulting in memory bottlenecks that severely impact computational efficiency. Faced with large-scale data and parameters, NPUs struggle to timely transfer data to computing cores due to insufficient memory bandwidth, causing computing units to often be in a waiting state for data, significantly lowering overall operational efficiency.
2. Architectural Design Limitations
(1) Poor Generality
As a dedicated chip designed for neural network inference, the NPU’s architecture optimization primarily targets specific matrix operations and convolution tasks, aiming to enhance the computational efficiency of these tasks. However, the training and inference processes of large models involve far more than simple matrix operations. The complex backpropagation algorithms during training require extensive gradient calculations and parameter updates, involving various optimization algorithms to adjust the training direction. The complexity and diversity of these general computing tasks far exceed the optimization scope of NPU architecture, leading to inefficiencies when NPUs handle such tasks. They cannot flexibly respond to various computational demands like general-purpose computing chips, exposing architectural limitations in complex scenarios of large model training and inference.
(2) Insufficient Precision Support
Large model training has extremely strict requirements for computational precision, with high-precision floating-point operations (such as FP32) being crucial for ensuring the accuracy and stability of model training ๐ง. During training, minor computational errors can accumulate over multiple iterations, ultimately affecting model performance. However, NPUs primarily support low-precision computations (such as INT8), designed to enhance computational efficiency and reduce power consumption in scenarios with relatively low precision requirements. In high-precision scenarios, NPUs perform poorly due to insufficient support for high-precision computations in their hardware architecture. This makes it difficult for NPUs to meet the stringent precision requirements of models during large model training, greatly limiting their application in this field.
3. Software Ecosystem Needs Improvement
(1) Scarcity of Development Tools and Frameworks
Compared to the mature ecosystem of GPUs, the NPU ecosystem is relatively weak, with fewer tools and frameworks available for developers. GPUs have widely used development tools such as CUDA and cuDNN, which have been developed and optimized over many years, providing high stability and compatibility, enabling developers to have a convenient and efficient development environment to quickly implement algorithms and optimize models. In contrast, the software stacks for NPUs are often developed independently by various manufacturers, lacking unified standards, leading to poor compatibility among different manufacturers’ NPU software stacks. Developers often need to spend a lot of time and effort dealing with software compatibility issues during development and adaptation, significantly increasing development difficulty and costs ๐ฉ.
(2) High Difficulty in Application Adaptation
Currently, the local programs and application scenarios supported by NPUs are quite limited. For example, applications that can call the NPU in AIPC are mostly simple background blurring, cutout, and other low-complexity functions. However, complex language model inference and training require high computational power and algorithm support, and current NPUs struggle to meet the demands of such complex applications in both hardware performance and software support. This makes it difficult for NPUs to fully leverage their potential in the face of diverse and complex applications, severely limiting their promotion and application in broader fields.
4. Prominent Cost and Power Consumption Issues
(1) Difficulty in Power Consumption Control
The low power consumption characteristic of NPUs allows them to be widely used in edge devices with power constraints, efficiently completing simple inference tasks with limited power in mobile or IoT devices, endowing them with intelligent functions. However, in the context of large model training and inference, the situation is entirely different. Large model computations require substantial computational resources, meaning NPUs must operate for extended periods at high intensity, significantly increasing the difficulty of power consumption control. To meet the computational demands of large models, NPUs may need to increase operational power, thereby sacrificing their proud low power consumption advantage, falling into a dilemma of balancing power consumption and performance ๐ต.
(2) Huge Cost Pressure
Edge devices are highly sensitive to costs, needing to strictly control costs while pursuing high performance. The deployment of large models relies on high-performance hardware, and if NPUs are to meet the performance requirements of large models, they may need to adopt more advanced process technologies, increase computing units, and expand memory capacity, which will undoubtedly significantly raise device costs. In contrast, while GPUs have higher power consumption, their performance advantages are significant, and their mature ecosystem provides developers with more choices and conveniences. Considering performance, ecosystem, and cost factors, GPUs offer a better cost-performance ratio in large model applications, putting NPUs under immense competitive pressure in the large model deployment market.
In Summary
In conclusion, existing NPUs exhibit significant deficiencies in hardware performance, architectural design, software ecosystem, and cost-power consumption, making it difficult to meet the high demands of large model training and inference. However, it is undeniable that NPUs possess unique advantages in inference tasks on edge devices, providing effective inference services with relatively low power consumption and cost in scenarios with stringent computational resource and power requirements. Looking ahead, with continuous technological advancements and gradual improvements in the ecosystem, NPUs are expected to play a greater role in the distributed deployment of large models and edge computing scenarios. By enhancing hardware performance, optimizing architectural design, enriching the software ecosystem, and better balancing cost and power consumption, NPUs are likely to open new pathways in the field of large model applications, injecting new momentum into the development of artificial intelligence ๐ช.
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