[Understand in one article] What is edge computing power?
Explore the key technologies for improving the performance of smart devices and gain an in-depth understanding of the full range of end-side computing power.
Core content:
1. The definition of end-side computing power and its core advantages
2. The technical framework and hardware support of end-side computing power
3. Application scenarios and development trends of end-side computing power
With the rapid development of the Internet of Things (IoT), artificial intelligence, and 5G technologies, edge computing is gradually becoming a key technology for improving the performance of smart devices and realizing intelligent applications. What is edge computing, what is its application value, and how is it different from cloud computing and edge computing?
This article introduces you to all aspects of edge computing power from the following six dimensions:
1. Definition of edge computing power
2. Technical framework of edge computing power
3. Application value and scenarios of edge computing power
4. Complementarity between end-side computing power and other technologies
5. Differences between edge computing power and related technologies
6. Development Trends and Future Challenges of Edge Computing
01
Definition of edge computing power
End-side computing refers to processing computing tasks directly on the terminal device, rather than relying on remote cloud servers or data centers. This computing model can give full play to the processing power of the device itself, reduce latency, save bandwidth resources, and improve data privacy protection in an environment without high-speed network support.
The core advantage of edge computing power lies in its localized data processing capabilities, which are particularly suitable for scenarios that are sensitive to latency and have high privacy requirements.
For example, in smart homes, voice recognition of smart speakers, local analysis of video surveillance, and real-time decision-making in autonomous driving all rely on end-side computing. Through local computing, devices can respond within a few milliseconds, far exceeding the traditional way of relying on the cloud.
02
Technical framework of edge computing power
The realization of edge computing power is inseparable from powerful hardware support, algorithm optimization and data security assurance.
Hardware Basics
The realization of edge computing power relies on strong hardware support, especially different types of processors (CPU, GPU, NPU, TPU). These processors have different advantages and are suitable for different computing tasks. The following specific examples will help you understand their characteristics and application scenarios.
CPU (Central Processing Unit)
Features: CPU is a general-purpose processor that is good at executing various complex instructions and tasks and is suitable for various computing tasks, especially control logic and serial tasks.
Application scenarios: The CPU is suitable for applications that require high flexibility and can handle background tasks such as operating systems and network management. For example:
Smartphone: The CPU is responsible for managing the execution of applications, the operation of the operating system, and coordination with other hardware.
Smart home: For example, in smart speakers, the CPU is responsible for processing simple user commands, such as volume adjustment, switch control, etc.
For example: Apple's A series chips. In the iPhone, the CPU is responsible for system-level tasks, such as UI rendering, phone dialing, and other daily operations.
GPU (Graphics Processing Unit)
Features: GPU was originally designed for graphics rendering, but with its development, it has become a powerful tool for highly parallel computing, especially suitable for parallel processing of large-scale data, such as image and video processing, deep learning training, etc.
Application scenarios: GPU is particularly suitable for parallel computing tasks and is widely used in fields such as images, videos, and AI reasoning.
Smart surveillance: In smart surveillance cameras, GPUs are used for real-time image recognition, such as facial recognition, object detection, etc.
Autonomous driving: Sensor data (such as radar and camera) in autonomous vehicles needs to be processed quickly. GPUs can efficiently handle these parallel tasks and make decisions in real time.
For example: Tesla's autopilot system. Tesla's FSD (full self-driving) chip uses GPU to process massive data from cameras, radars, and lidars in real time to identify objects on the road and plan paths.
NPU (Neural Network Processing Unit)
Features: NPU is a processor designed specifically for artificial intelligence reasoning tasks, which can efficiently execute neural network algorithms. It is more focused on AI tasks than GPU, has lower power consumption and higher reasoning speed, and is especially suitable for real-time AI processing in edge devices.
Application scenarios: NPU is very suitable for scenarios that require fast AI reasoning, especially in end devices, such as speech recognition, face recognition, motion recognition, etc.
Smartphones: For example, the NPU in Huawei's Kirin chip is used for the phone's AI photography, face unlocking, voice assistant and other functions.
Smart speakers: NPU is used to process voice commands and natural language processing to quickly respond to user needs.
For example: The NPU in Huawei's Kirin processor. In Huawei's mobile phones, the NPU is responsible for quickly processing AI tasks, such as real-time image enhancement and voice recognition, to improve user experience.
TPU (Tensor Processing Unit)
Features: TPU is an accelerator designed for machine learning, especially deep learning tasks. Similar to NPU, TPU is optimized for tensor operations (common matrix operations in machine learning models), which can greatly improve training speed and inference efficiency.
Application scenarios: TPU is mainly used for efficient deep learning tasks and is suitable for AI training and reasoning tasks that require a lot of computing power.
Autonomous driving: Similar to GPUs, TPUs can accelerate AI reasoning in autonomous driving systems and help process and analyze road information in real time.
Data center AI reasoning: In cloud computing and big data centers, TPU can be used to process large-scale deep learning reasoning tasks.
For example: Google Edge TPU, the Edge TPU launched by Google is a small TPU designed specifically for edge devices to accelerate AI reasoning tasks. For example, in a smart camera, the Edge TPU can quickly analyze video streams and respond in real time.
Through these specific examples, we can see that each processor plays a unique role in different application scenarios. The choice of hardware usually depends on the different application requirements, such as whether high parallel computing is required, whether it needs to be optimized for AI tasks, etc. These hardware are not mutually exclusive, but can complement each other and work together in different devices and scenarios.
Algorithm optimization
In the realization of end-side computing power, algorithm optimization is a crucial link. Since the computing resources of end-side devices are usually limited, how to efficiently run complex tasks, especially improving performance while ensuring high accuracy, is the core goal of optimization. The following are three common optimization methods, each with different characteristics and effects.
Quantization
Quantization is a technique that reduces resource consumption by reducing the computational precision of the model. Specifically, quantization converts the floating point precision in the model (such as 32-bit floating point numbers) into lower fixed point numbers (such as 8-bit integers), thereby significantly reducing memory usage and speeding up calculations. The quantized model can usually significantly improve memory usage and computational speed, but may result in a certain loss of precision.
To understand this, imagine that when a high-definition image is compressed to a lower resolution, the image quality is reduced, but the file size is smaller and loads faster. Similarly, quantizing the optimized model can speed up the inference process while reducing storage requirements.
Experiments show that the MobileNetV2 model optimized by quantization has increased inference speed by about 40%, reduced memory usage by about 60%, and the accuracy drop is usually controlled within 3%. Although there will be a slight drop in accuracy, quantization can effectively solve the problem of limited computing and storage capabilities of end-side devices, and is particularly suitable for models that need to run on mobile devices or embedded systems.
Pruning
Pruning technology reduces the size of the model by removing unimportant connections or neurons in the neural network. Pruning can not only reduce the amount of calculation, but also effectively reduce memory usage. Unlike quantization, pruning structurally modifies the architecture of the model and removes parts that do not affect the final result. This can effectively increase the inference speed and reduce the memory burden. The pruned model usually has a faster inference speed and significantly reduces memory requirements, but it may also have a certain impact on accuracy.
For example, pruning can be likened to trimming the branches and leaves of a tree, removing unnecessary parts to make the tree lighter and more efficient.
In practical applications, experiments show that the ResNet-50 model optimized by pruning has increased inference speed by about 25%, reduced memory usage by 50%, and maintained accuracy above 95%. Pruning is very suitable for tasks that have high requirements for memory and inference speed.
Knowledge Distillation
Knowledge distillation is a method that transfers knowledge from a large deep neural network (usually called a "teacher model") to a smaller network (a "student model"). In this way, the optimized small model can maintain a high accuracy rate under limited computing resources. Unlike quantization and pruning, the core of knowledge distillation is to allow the smaller model to learn more complex knowledge through "teaching", so as to get as close to the large model as possible in performance.
Imagine that teachers simplify complex knowledge and pass it on to students. Although students learn less, they learn more efficiently.
For example, in speech recognition applications, the student model optimized using knowledge distillation has an accuracy almost equivalent to that of the teacher model, while increasing the inference speed by 40%. This approach is particularly suitable for applications that need to run on devices with limited computing resources.
In general, algorithm optimization plays a vital role in the application of edge computing power. Quantization, pruning, and knowledge distillation are three common optimization strategies that effectively improve the reasoning speed and computing efficiency of the model by reducing model accuracy, removing redundant calculations, and transferring knowledge, while minimizing the loss of accuracy. These optimization methods enable edge devices to perform efficient artificial intelligence tasks even with limited computing and storage resources. With the continuous advancement of technology, these optimization methods will continue to improve in the future to help edge computing power further improve application performance, especially in resource-limited scenarios such as mobile devices and embedded devices.
Data transmission and security
An important feature of edge computing is local data processing, which plays a key role in ensuring data security and privacy protection. Compared with the traditional cloud computing model, edge computing greatly reduces the risk of data leakage during transmission by avoiding transmitting data to remote servers. Therefore, edge computing has obvious advantages in data transmission and security.
Localization of data transfer
One of the core advantages of edge computing is that it moves data processing tasks to local devices instead of uploading data to the cloud for processing. Since data does not need to be transmitted to remote servers, edge devices can reduce the risk of data leakage and abuse. Especially when processing sensitive user data, such as facial recognition, fingerprint recognition, and voice recognition, edge computing can quickly process data locally without relying on cloud storage or remote computing. This localized data processing method is the basis for ensuring data privacy protection.
Encryption and Privacy Protection
In order to ensure that data is not leaked when processed locally, end-side computing power usually combines multiple encryption technologies for privacy protection. During local computing, the end-side device will encrypt the storage and transmission of sensitive data to ensure that the data remains secure during use. For example, in applications such as facial recognition and fingerprint recognition, the user's biometric data will be encrypted and stored in the device's secure storage area. This encrypted data can usually only be decrypted and used within the local device and will not be exposed to the external environment.
Typical application case: Face ID technology
Apple's Face ID technology is a classic example of how edge computing can improve data security. The technology uses the Neural Engine in the iPhone's built-in A-series processors (such as the A11, A12, and A13 chips) to process and store the user's facial data directly on the device without uploading the image to the cloud. The user's facial data is encrypted and stored only in a secure area within the device, ensuring that the data cannot be accessed externally even if the device is lost or stolen.
In the implementation of Face ID, the device uses a combination of "local computing" and "encrypted storage". Face ID technology not only improves the response speed of facial recognition, but also reduces the risk of data leakage through localized data processing. In addition, Apple also isolates and stores facial recognition data through the "Secure Enclave", further enhancing data privacy protection.
Typical application case: Voice assistant
In the application of voice assistants, end-side computing power also plays an important role in ensuring data security. Traditionally, voice assistants (such as Siri, Xiao Ai, etc.) upload users' voice data to the cloud for processing. Although this method can improve the accuracy of voice recognition, it also increases the risk of data leakage. With the advancement of technology, more and more voice assistants are migrating some computing tasks to local devices for processing to ensure that data does not leave the device and reduce the risk of privacy leakage.
For example, Apple's Siri has begun to migrate some of its voice recognition functions to devices such as iPhone, iPad, and HomePod, and complete data analysis locally through built-in processors. This approach not only improves the response speed of voice recognition, but also avoids transmitting the user's voice data to a remote server. Through encrypted storage and localized computing, Siri can achieve secure processing of voice data on the user's device.
Technical assurance: compliance and privacy protection
Edge computing can not only improve user privacy protection, but also help enterprises meet the increasingly stringent data privacy laws and regulations in various countries. For example, the EU's General Data Protection Regulation (GDPR) requires enterprises to take high-standard protection measures when processing user data. By moving data processing to the local area, edge computing can help enterprises avoid exposing large amounts of sensitive data to the external environment, thereby reducing the risk of data leakage while ensuring compliance.
Many countries and regions have put forward strict requirements for the privacy protection of personal data, especially in sensitive fields such as finance, medical care and government. The end-side computing power effectively reduces the possibility of data leakage through technical means such as localized processing, encrypted storage and encrypted transmission, and helps enterprises comply with legal provisions on data privacy protection around the world.
In summary, by transferring data processing tasks to local devices, end-side computing effectively reduces the risk of sensitive data leakage during transmission. At the same time, through encryption technology and local storage, end-side computing provides strong privacy protection for user data. With the increasingly stringent laws and regulations on privacy protection, end-side computing will play a more important role in the future technical framework, especially when processing personal data that requires high confidentiality. End-side computing is undoubtedly a key technology to improve data security and privacy protection.
03
Application value and scenarios of edge computing power
As a technology for performing calculations on local devices, edge computing can improve performance in a variety of application scenarios. By performing real-time calculations on the device side, edge computing has shown great advantages in improving response speed, ensuring privacy and security, saving bandwidth, and reducing energy consumption. It is particularly suitable for scenarios with high real-time requirements, high security requirements, and low bandwidth dependence. The following are typical applications of edge computing in multiple industries and their specific value:
Low latency and real-time response
End-side computing power can greatly reduce the delay caused by transmitting data to the cloud, which is especially important for applications with extremely high real-time requirements.
For example, self-driving cars need to analyze data from multiple sensors (such as lidar, cameras, radar, etc.) in real time and make driving decisions quickly. Any delay will bring potential safety hazards. By transferring computing tasks to the on-board computing platform, the end-side computing power can respond quickly within milliseconds, analyze the surrounding environment in real time and make decisions, such as avoiding obstacles or performing emergency braking, to ensure driving safety.
Similarly, augmented reality (AR) and virtual reality (VR) applications also have extremely high requirements for low latency. Traditional cloud computing solutions require data to be transmitted to the cloud and then returned to the device, which will cause unnecessary delays and affect the user experience. The end-side computing power completes tasks such as image recognition, location tracking, and real-time rendering on the local device to ensure that the user's immersive experience when using AR glasses or VR helmets is seamless.
Privacy protection and data security
Privacy protection is a key issue that users are increasingly concerned about when using smart devices. The end-side computing power processes data locally, reducing the risk of data leakage during transmission and improving the security of privacy protection. Especially in scenarios such as smart speakers, smart home cameras, and wearable devices, the devices process sensitive data (such as voice information, facial recognition, and health data) through local computing, avoiding data leakage incidents that may occur when uploading this data to the cloud.
For example, smart door locks use end-side computing power to perform facial recognition locally instead of uploading facial data to the cloud for processing. This not only improves response speed, but also enhances data privacy protection. In the field of health monitoring, smart watches and health monitoring devices analyze health data through local computing, reducing the need to upload such sensitive data to the cloud, further improving users' sense of privacy and security.
Bandwidth saving and independent operation
The end-side computing power greatly reduces the dependence on the cloud by completing computing tasks locally, which is particularly advantageous in situations where bandwidth is limited or the network is unstable. The device can operate efficiently without a continuous network connection, avoiding the problem of bandwidth bottlenecks.
For example, in remote monitoring and security systems, network cameras use end-side computing power for video monitoring and intelligent analysis. The camera can not only capture real-time video, but also complete tasks such as motion detection, face recognition, and object tracking locally, detect abnormalities in a timely manner, and respond. Even in the event of network interruption or insufficient bandwidth, the device can still work independently and make necessary decisions, such as triggering an alarm or notifying the user.
Similarly, drones and service robots can also complete autonomous flight and mission execution in an environment without a stable network through edge computing. Edge computing can help these devices analyze sensor data in real time locally and make quick flight decisions or task processing without relying on the cloud for remote computing. This allows these devices to operate stably in extreme environments such as remote areas and underground mines, ensuring efficient operation.
Scalability and flexibility
Another important feature of edge computing is its excellent scalability and flexibility. The device can dynamically allocate computing resources according to the complexity of the actual task, so as to efficiently complete various tasks.
For example, in the fields of industrial manufacturing and robotics, edge computing can adjust computing resources according to the complexity of the task to ensure that the equipment completes the work efficiently. Industrial robots make decisions in real time based on sensor feedback to perform tasks such as object grasping and obstacle avoidance, which rely on highly accurate calculations. With edge computing, robots can perform these computing tasks locally without relying too much on the cloud, thereby improving work efficiency and reducing the delay in interacting with the cloud.
In addition, edge computing also supports automatic upgrades and functional expansion of devices. Device manufacturers can add new computing modules to devices or optimize existing computing architectures based on actual needs, thereby improving the overall performance and processing capabilities of the devices. This flexible scalability enables edge computing to adapt to more dynamically changing usage scenarios, improving its long-term sustainability and breadth of application.
In general, edge computing is widely used in fields such as autonomous driving, augmented reality, smart home, health monitoring, and security systems by improving response speed, protecting privacy, saving bandwidth, and increasing device independence and flexibility. These applications demonstrate the huge potential of edge computing in improving the performance of smart devices, enhancing user experience, and coping with complex environments.
04
Complementarity between end-side computing power and other technologies
End-side computing is not the only technical solution. It is complementary to cloud computing, edge computing and other technologies. In the following scenarios, the combination of different technologies can achieve the best results.
Combination of end-side computing power and cloud computing
The combination of edge computing and cloud computing can optimize real-time data processing and large-scale computing. Edge computing performs real-time data processing on local devices and is suitable for scenarios that require low latency and high response, such as smart speakers and autonomous driving, while cloud computing can handle more complex computing tasks and provide large-scale data storage capabilities.
Application scenario: Smart car
End-side computing power: In smart cars, data generated by on-board sensors (such as lidar, cameras, radar, etc.) needs to be processed immediately. End-side computing power can analyze sensor data in real time locally, such as obstacles around the vehicle, driving paths, traffic signals, etc. By processing on the on-board computing platform, the car can make decisions within milliseconds to ensure driving safety.
Cloud computing: Although end-side computing can process most data in real time, smart cars still need cloud computing to handle large-scale historical data analysis, complex AI training, and cross-vehicle data sharing. For example, the cloud can provide vehicles with services such as map updates, driving behavior learning, and weather forecasts to ensure that vehicles can continuously optimize their performance during long-term operation.
Combined advantages: End-side computing provides real-time response in smart cars by reducing latency and bandwidth consumption, while cloud computing is responsible for tasks with high computational complexity and large-scale data storage. The combination of the two not only improves the vehicle's autonomous driving capabilities, but also ensures the long-term scalability of the system.
Combination of client-side computing power and edge computing
Edge computing transfers data processing from centralized data centers to edge nodes closer to data sources, while end-side computing further compresses computing to the device level, reducing reliance on edge nodes. This hierarchical computing approach is particularly effective in scenarios that require low latency and high privacy protection.
Application scenario: Smart city
Edge computing: In smart cities, tens of thousands of sensors and devices continuously generate massive amounts of data. Edge computing can transfer data processing tasks from the cloud to edge nodes that are closer to the data source. These edge nodes reduce network burden and improve response speed by processing data in real time. For example, surveillance cameras in cities can complete real-time video analysis through edge computing, identify abnormal behavior or traffic conditions, and reduce the time and bandwidth requirements for data transmission to remote data centers.
End-side computing power: Based on edge nodes, end-side computing power further sinks computing tasks to the device level. For example, in a smart parking lot, license plate recognition cameras can complete recognition processing locally without sending image data to edge computing nodes or the cloud. With end-side computing power, devices can respond quickly and make decisions, such as automatically opening doors and recording parking time.
Combined advantages: Edge computing and end-side computing complement each other. Edge computing is responsible for preprocessing and simple reasoning of data in a local area, while end-side computing is responsible for further reducing the network dependence of computing and achieving real-time and efficient device-level decision-making. The combination of the two effectively improves the real-time performance and energy efficiency in smart cities.
In summary, edge computing can significantly improve real-time responsiveness, reduce bandwidth consumption, and enhance data privacy protection by moving computing tasks to local devices. It is particularly suitable for application scenarios that require low latency and high privacy requirements. When combined with cloud computing and edge computing, it can exert stronger advantages and adapt to diverse computing needs.
05
The difference between edge computing power and related technologies
Although edge computing is not a computing technology in the traditional sense, it is closely related to traditional computing, cloud computing, edge computing and other technologies, and complements each other in modern computing architecture. In order to fully understand the advantages and limitations of edge computing, we need to compare it with these technologies, so that we can more clearly understand the role of different technologies in various application scenarios, identify their complementarity and synergy, and thus help us choose the most suitable technical solution to achieve the best performance and effect.
Edge computing power vs traditional computing
Traditional computing usually relies on the central processing unit (CPU) to process tasks, and transmits data through the network to a central server or data center for centralized computing. The advantage of this computing model is that it can use the powerful computing power of the cloud to process complex tasks, but it is highly dependent on network connections and is prone to showing its disadvantages in application scenarios with high real-time and bandwidth requirements.
The end-side computing power transfers the computing and processing tasks to the local device, without the need to transmit data remotely, avoiding the impact of network delays and reducing the dependence on bandwidth. It is suitable for applications with real-time response and unstable network connections, and can perform instant computing on the device side.
Edge computing power vs fog computing
Fog computing is a distributed computing architecture between edge computing and cloud computing. It deploys computing resources at the edge of the network, shortening the distance to the device to reduce latency and bandwidth consumption. Although both fog computing and edge computing can process data close to the device, their computing methods and applicable scenarios are different.
The end-side computing power completely transfers computing tasks to local devices for processing, avoiding any network dependence, achieving low latency and high real-time performance, and is particularly suitable for applications with high real-time response requirements, such as autonomous driving, smart homes, and industrial control. Although fog computing also relies on edge nodes, it still needs to be calculated through network connections. Real-time performance is affected by network conditions, and edge nodes still need to process a certain degree of external computing tasks.
Edge computing power vs. cloud computing
Cloud computing relies on remote data centers to process and store data, and is suitable for large-scale and complex computing tasks. The advantage of cloud computing is that it can make full use of powerful computing resources, especially when large amounts of data need to be analyzed. However, when tasks have strict requirements on latency, the disadvantages of cloud computing gradually become apparent, because data must be transmitted from the device to the cloud for processing, and such transmission delays will affect the effect of real-time response.
In contrast, edge computing can achieve instant computing by transferring computing tasks to the local device for processing, avoiding the delay caused by network transmission. This method is particularly suitable for applications with high real-time requirements, such as autonomous driving and industrial automation, and can ensure the rapid response of the system. At the same time, edge computing reduces the dependence on bandwidth and is suitable for environments with limited bandwidth.
Client-side computing power vs. edge computing
Edge computing transfers data processing tasks from data centers to network nodes closer to devices, which can reduce latency and optimize bandwidth usage, and is suitable for applications that require real-time response. Edge computing solves the problems of cloud computing latency and bandwidth bottlenecks by performing calculations close to the data source, but it still relies on network connections and communication between edge nodes.
The main difference between end-side computing and edge computing lies in the location and dependencies of computing. End-side computing completely transfers computing tasks to the device for processing, without relying on external networks or edge nodes. It can achieve lower latency and higher self-sufficiency, and is suitable for applications with extremely high requirements for real-time and independence, such as autonomous driving, smart homes, and industrial control. In these scenarios, the advantages of end-side computing are particularly prominent because it can continue to operate without network connections or edge node support.
In contrast, edge computing still needs to rely on edge nodes and network connections. Although computing resources are closer to data sources, its real-time performance and processing capabilities are affected by network quality, especially when the network is unstable or the edge nodes are heavily loaded, which may affect the response speed of the application and the stability of the system.
06
Development Trends and Future Challenges of Edge Computing
As a key component of modern computing architecture, the future development of edge computing power will be driven by multiple factors such as hardware, algorithms, and network technology.
Development Trend
Popularization of hardware acceleration: With the continuous advancement of dedicated hardware accelerators (such as neural network processing units (NPUs) and tensor processing units (TPUs), the hardware performance of edge computing will be significantly improved. These dedicated chips are optimized for specific computing tasks, so that the processing power of devices is no longer limited by the performance bottleneck of traditional central processing units (CPUs). In the future, more and more devices will be equipped with high-performance AI acceleration chips to support more complex computing tasks and promote the popularization and application of edge computing.
Further optimization of algorithms: The advantages of edge computing power are not only reflected in the hardware level, but the optimization of software and algorithms is also crucial. With the continuous evolution of deep learning models and other AI algorithms, edge computing power will be able to carry more complex computing tasks in the future. Further optimization of algorithms will enable edge devices to run more efficient and powerful tasks with relatively limited hardware resources, and the requirements for hardware will gradually decrease. This means that even devices with limited resources can efficiently perform complex tasks such as deep learning, image processing, and speech recognition.
Deep integration of 5G and AI: The high speed and low latency of 5G networks will greatly enhance the application scenarios of edge computing, especially in the collaborative work of edge computing and smart devices. The high bandwidth and low latency of 5G make the collaborative computing between edge devices and the cloud and other devices more efficient, promoting the development of more flexible and efficient smart devices. For example, smart devices such as self-driving cars and industrial robots can use edge computing to process real-time data with the support of 5G networks, and work with other devices to improve the response speed and processing power of the overall system.
Future Challenges
Although edge computing has a bright future, it still faces a series of challenges. First, the hardware cost of edge computing equipment is high, especially when it is required to be equipped with a dedicated AI acceleration chip, the equipment manufacturing cost may increase significantly. Second, edge devices are usually small in size and have limited heat dissipation space, which will affect the service life and stability of high-performance hardware. Therefore, how to solve the heat dissipation problem of equipment while ensuring computing performance will be a major problem for future development.
In addition, as the complexity of the algorithm increases, the computing and storage requirements of the end-side computing devices also increase, which places higher requirements on the hardware resources of the devices. How to balance performance and power consumption with limited hardware resources and ensure that the device can maintain low power consumption when working under high load will be a key issue in future technological development.
The rise of edge computing power is profoundly changing the computing model of smart devices. Through efficient, low-latency local computing, edge computing power improves user experience and provides stronger protection for privacy. With the continuous optimization of hardware technology and algorithms, edge computing power will play a key role in more fields, driving smart devices to develop in a more intelligent and efficient direction. Despite challenges such as hardware cost and power consumption, as technology matures, edge computing power is expected to become the core driving force of smart devices in the future, bringing more innovative applications and experiences.
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