(Comprehensive report by Electronic Enthusiasts Network) NPU is a hardware processor specifically designed to accelerate neural network computations. With the rapid development of artificial intelligence and deep learning technologies, traditional CPUs and GPUs struggle to handle complex neural network computations, which is why NPUs were designed.
As early as 2011, Google proposed the use of large-scale neural networks for image recognition and achieved significant results in the 2012 ImageNet competition, marking the rise of deep learning technology.
With the continuous development of deep learning technology, traditional CPUs and GPUs have gradually become inadequate for processing large-scale neural network computations, creating an urgent need for more efficient computing units.
To meet the demands of deep learning computations, NPU emerged. NPU is a processor specifically designed to accelerate neural network computations, utilizing a parallel computing architecture that significantly improves computational efficiency.
In 2016, Google released the TPU chip, the world’s first commercial NPU product. TPU demonstrated outstanding performance in deep learning computations, laying the foundation for the commercialization of NPU. Subsequently, tech giants like NVIDIA, AMD, and Intel also launched their own NPU products, driving rapid development in the NPU market.
In the smartphone sector, the application of NPU has also gradually emerged. Around 2016, Cambricon released the world’s first commercial terminal AI processor, providing strong hardware support for mobile AI computations.
In 2017, Huawei first integrated the Kirin 970 processor with an AI module in the Mate 10, marking the arrival of the smartphone AI era. Following this, manufacturers like Apple and Samsung also launched smartphones equipped with NPU.
As technology continues to advance, the performance and functionality of NPU have significantly improved. Chip manufacturers like Qualcomm and MediaTek have invested heavily in NPU research and development, launching multiple generations of NPU products, such as Qualcomm’s Hexagon NPU series. The Hexagon NPU excels in performance, power consumption, and flexibility, providing strong AI computing capabilities for smartphones, IoT devices, and other terminal products.
NPU Performance Indicators
The performance of NPU is typically influenced by multiple factors, with computing power being a key indicator of NPU performance, usually measured in terms of floating-point operations per second (FLOPS) or integer operations per second (IOPS). In AI chips, computing power is often represented in TOPS (Tera Operations Per Second).
The level of computing power directly affects the speed and efficiency of NPU when processing neural network tasks. The higher the computing power, the more calculations the NPU can complete in the same time frame, resulting in faster processing of complex neural network tasks.
The working frequency of NPU is another important performance indicator, affecting the processor’s computation speed. Frequency depends on the manufacturing node and NPU design, and there may be variations between different NPUs. A higher frequency allows the NPU to perform more calculations in a unit of time, thus improving processing speed. However, increasing frequency can also lead to higher power consumption and heat generation, impacting battery life and user experience. Therefore, it is crucial to find a balance between performance and power consumption when designing NPU.
NPU possesses a large number of parallel computation cores, enabling efficient parallel processing. This is a significant advantage of NPU when handling neural network tasks. The stronger the parallel computing capability, the more computation tasks or data points the NPU can process simultaneously, thereby enhancing overall computational efficiency. This is particularly important for processing large-scale neural networks and complex datasets.
As a component of embedded devices, the power consumption of NPU is also a performance indicator that cannot be ignored. Power consumption refers to the energy consumed by the NPU during operation. Low power consumption makes NPU more suitable for mobile devices and edge computing scenarios. When designing NPU, it is essential to consider how to maintain high performance while reducing power consumption to extend device usage time and enhance user experience.
Precision refers to the granularity of calculations; generally, higher precision leads to greater model accuracy, but requires higher computational intensity. Common precisions include 32-bit and 16-bit floating-point precision, as well as faster low-precision low-power models that typically use 8-bit and 4-bit integer precision. Choosing the appropriate precision is critical for balancing NPU performance and power consumption. High precision can improve model accuracy but increases computational complexity and power consumption; while low precision can reduce power consumption and computational complexity, it may sacrifice some model accuracy.
Memory bandwidth determines the speed at which NPU can retrieve data from memory. If memory bandwidth is insufficient, the NPU may remain idle waiting for data, thereby reducing overall performance.
Software optimization is also an important factor influencing NPU performance. By optimizing algorithms and code, the computational resources of the NPU can be fully utilized, improving computational efficiency and accuracy.
In modern chip design, NPU is often integrated into SoCs as an important component. This integration allows SoCs to maintain their high integration, low power consumption, and other advantages while further expanding their application capabilities in artificial intelligence.
For example, in applications such as smartphones and IoT devices, NPU is often integrated into SoCs to provide efficient AI processing capabilities. This integration not only helps enhance the overall performance of devices but also reduces design costs and power consumption.
When NPU is integrated into SoC, it can work collaboratively with other functional modules (such as CPU, GPU, memory, etc.) to complete complex computational tasks. The CPU handles general computing tasks, the GPU handles graphics rendering and large-scale parallel computing tasks, while the NPU focuses on neural network computing tasks. This collaborative approach can fully leverage the advantages of each functional module, improving overall computational efficiency.
With the continuous development of artificial intelligence technology, the application scenarios of NPU in SoC are becoming increasingly widespread. In smartphones, NPU can be used for AI photography, voice assistants, and other functions; in IoT devices, NPU can be used for smart recognition, automatic control, and other functions; in autonomous vehicles, NPU can be used for visual recognition, path planning, and other tasks.
Disclaimer: This article is a comprehensive report by Electronic Enthusiasts Network. Please indicate the source above when reprinting. For group discussions, please add WeChat elecfans999, for submission inquiries, please send an email to [email protected].
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