NPU: A new engine for computing power in the AI era
1. The basic concept and architecture of NPU
NPU stands for Neural Processing Unit. It is a hardware accelerator specifically designed to accelerate neural network operations. The core concept of NPU is to simulate the working principle of human brain neural networks, and accelerate complex calculations such as large-scale matrix operations and convolution operations in deep neural networks through massively parallel processing units (similar to neurons) and efficient interconnected structures (similar to synapses).
The architecture of the NPU is significantly different from that of traditional CPUs and GPUs. The CPU is based on the von Neumann architecture, emphasizing instruction-level parallelism and pipelined processing, and is suitable for a wide range of computing tasks. GPUs, on the other hand, are based on array processing architectures and are good at handling a large number of parallel computing tasks, such as graphics rendering and scientific computing. NPU adopts the architecture of "data-driven parallel computing", and is especially good at processing massive multimedia data such as video and images. It usually includes a multiplication and addition module, an activation function module, a two-dimensional data operation module and a decompression module.
The multiplication and addition module is used to calculate matrix multiplication, convolution, point multiplication and other functions; The activation function module uses the highest 12th-order parameter fitting method to realize the activation function in the neural network. The two-dimensional data operation module is used to realize the operation of a plane, such as downsampling, plane data copying, etc.; The decompression module is used to decompress the weighted data to solve the characteristics of small memory bandwidth in IoT devices.
2. Application scenarios of NPUs
NPUs show great potential in a number of areas, especially in scenarios where large amounts of data need to be processed efficiently. Here are some of the main application scenarios in which NPU performs:
Smartphones: In smartphones, NPU can be used for functions such as image recognition, voice recognition, and face unlocking. For example, NPU can speed up image recognition algorithms, enabling mobile phones to recognize objects and scenes in images more quickly and accurately. This is very helpful for applications such as taking photos, image search, augmented reality (AR), and virtual reality (VR). NPU can also improve the efficiency and accuracy of speech recognition algorithms, making it more convenient for users to operate their phones through voice commands.
Smart home: In smart home, NPU can be used in smart speakers, smart cameras and other devices to achieve voice control, intelligent monitoring and other functions. For example, smart speakers can accelerate speech recognition and natural language processing through NPU to achieve a more natural and intelligent interactive experience. The smart camera can accelerate image processing and video through the NPU to achieve real-time monitoring and intelligent alarm.
Autonomous driving: In the field of autonomous driving, NPU can be used to identify road conditions, perceive the surrounding environment, and improve the safety and reliability of autonomous driving. For example, self-driving cars can accelerate image recognition and sensor data processing through NPU to enable real-time situational awareness and decision-making.
Medical: In the medical field, NPU can be used for medical imaging diagnosis, risk assessment, and other applications. For example, NPUs can accelerate the processing of medical images and improve the accuracy and efficiency of diagnosis. NPUs can also be used in fields such as genetics and drug research and development to promote the advancement of medical technology.
Finance: In the financial field, NPU can be used for applications such as risk assessment and robo-advisory. For example, NPU can speed up the processing of financial data and improve the accuracy and efficiency of risk assessment. NPU can also be used as robo-advisors to provide personalized investment advice.
3. Advantages of NPU
NPU exhibits multiple advantages when handling AI tasks, making them ideal for AI computing. Here are some of the main benefits of NPU:
High performance: NPU uses a specially optimized hardware architecture and algorithms to perform neural network calculations more efficiently. Traditional CPUs and GPUs waste a lot of energy and computing resources when processing AI tasks. Through a highly parallel architecture, NPU can process a large amount of data at the same time, thereby improving processing speed and efficiency.
Low power consumption: Through a dedicated instruction set and compact circuit design, the NPU can significantly reduce power consumption and extend the flight time of the device while maintaining high performance. This is especially important for mobile and IoT devices.
Highly customizable: NPU is highly customizable and can be customized to the needs of specific applications, providing greater flexibility and adaptability. For example, NPU can adjust the hardware architecture and algorithms to achieve the best computing efficiency and performance according to different neural network structures and computing needs.
Real-time performance: NPU can provide high-throughput and low-latency computing performance, which is suitable for scenarios that require real-time processing of large amounts of data. For example, in applications such as autonomous driving and intelligent monitoring, NPU can achieve real-time environmental awareness and decision-making, improving the response speed and reliability of the system.
Fourth, the future development trend of NPU
With the rapid development of artificial intelligence technology, NPU is also constantly evolving and innovating. NPU will show greater development potential in the following aspects:
Higher computing performance: Future NPUs will continue to improve computing performance to cope with increasingly complex AI tasks. For example, the new generation of NPU will introduce more compute cores and more efficient algorithms to improve compute efficiency and performance.
Lower power consumption: Future NPUs will further reduce power consumption to meet the needs of mobile devices and IoT devices. For example, by optimizing the circuit design and instruction set, NPU can significantly reduce power consumption and extend the device's flight time while maintaining high performance.
Wider application scenarios: In the future, NPUs will be applied to more fields and scenarios. For example, NPU will be widely used in intelligent manufacturing, smart cities, smart agriculture and other fields to promote the intelligent development of all walks of life.
Stronger programmability: Future NPUs will have stronger programmability to adapt to different AI tasks and application needs. For example, by introducing programmable logic units and a flexible instruction set, the NPU can be customized and optimized to meet the needs of the specific application.
A more complete ecosystem: In the future, NPUs will form a closer collaborative working mechanism with other computing units (such as CPUs and GPUs) to build a more complete computing ecosystem. For example, through a heterogeneous computing architecture, NPU can work with CPUs and GPUs to complete complex AI tasks and improve the overall performance and efficiency of the system.
5. NPU and heterogeneous computing
Heterogeneous computing refers to the simultaneous use of multiple different types of processors in a single computing system to take full advantage of each. As a specialized AI accelerator, NPU often works in tandem with CPUs and GPUs to form heterogeneous computing architectures. The following are some application scenarios of NPU in heterogeneous computing:
Smartphones: In smartphones, the NPU, CPU, and GPU work together to form a heterogeneous computing architecture. The CPU is responsible for general computing tasks and system management, the GPU is responsible for graphics rendering and parallel computing, and the NPU is responsible for the acceleration of AI tasks. For example, in a photographic application, the NPU can accelerate image recognition and processing, the GPU is responsible for image rendering, and the CPU is responsible for overall coordination and control.
AI PC: In an AI PC, the NPU, CPU, and GPU work together to form a heterogeneous computing architecture. The CPU is responsible for general computing tasks and system management, the GPU is responsible for graphics rendering and parallel computing, and the NPU is responsible for the acceleration of AI tasks. For example, in video conferencing, the NPU can accelerate image processing and speech recognition, the GPU is responsible for video rendering, and the CPU is responsible for overall coordination and control.
Autonomous driving: In autonomous driving systems, the NPU, CPU, and GPU work together to form a heterogeneous computing architecture. The CPU is responsible for general computing tasks and system management, the GPU is responsible for graphics rendering and parallel computing, and the NPU is responsible for the acceleration of AI tasks. For example, in autonomous vehicles, NPU can accelerate image recognition and sensor data processing, GPUs are responsible for environment modeling and path planning, and CPUs are responsible for overall coordination and control.
6. Challenges and opportunities for NPU
Although NPU has shown great potential in AI computing, its development also faces some challenges. Here are some of the challenges that NPU may encounter during its development and how to address them:
Ecosystem building: The ecosystem of NPU is not yet perfect, and it lacks mature development tools and software support like GPUs. In order to solve this problem, NPU vendors need to increase investment in development tools and software ecosystems, and provide a more complete development environment and support.
Standardization issues: The definition and naming of NPUs are not uniform, and there are differences in architecture and functions between NPUs of different vendors. In order to solve this problem, the industry needs to develop unified standards and specifications to promote the standardization development of NPUs.
Technological innovation: NPU technology is still evolving, and technological innovation is needed to cope with increasingly complex AI tasks. In order to solve this problem, NPU manufacturers need to increase R&D investment to promote technological innovation and progress.
Market competition: The NPU market is highly competitive and faces competitive pressure from traditional processors such as CPUs and GPUs. In order to solve this problem, NPU manufacturers need to continuously improve product performance and competitiveness, and provide more efficient and low-power solutions.
Seven
As a processor specially designed for AI computing, NPU is gradually becoming a new engine of computing power in the AI era. Through high performance, low power consumption and high customization, NPU has shown strong application potential in many fields such as smart phones, smart homes, autonomous driving, medical care, and finance. With the continuous development of technology, NPU will show greater development potential in terms of computing performance, power consumption, application scenarios, programmability, and ecosystem. Despite some challenges, NPU is expected to play an increasingly important role in AI computing through technological innovation and ecosystem building, promoting the intelligent development of all walks of life.