Technology and Applications

As Artificial Intelligence (AI) is increasingly applied in edge computing, running complex AI models imposes higher demands on device hardware. RAM (Random Access Memory), being a critical hardware component, directly impacts the efficiency and stability of AI inference. So, how much RAM is typically required by edge computing devices to run complex AI models? This article analyzes the factors determining …

With the rapid development of the Internet of Things (IoT), Artificial Intelligence (AI), and industrial automation, edge computing devices are increasingly deployed in scenarios that require high-intensity and prolonged operations. Under such high-load conditions, the computational stability and reliability of these devices become critical to overall system performance and application continuity. So, how can edge computing devices ensure stability and …

As edge computing becomes widely adopted in industrial automation, intelligent transportation, and the Internet of Things (IoT), real-time performance has emerged as a critical metric for evaluating device capabilities. Under high-load conditions, edge devices must handle complex tasks from multiple sensors, devices, or data streams while maintaining low latency and high reliability. So, how can the real-time performance of edge …

In the era of rapid advancement in modern industries and the Internet of Things (IoT), edge computing devices are gaining attention for their ability to process data locally, reduce latency, and enhance real-time performance. Supporting external devices and sensors is a critical feature of edge computing devices, enabling efficient multi-input data handling, which directly impacts overall application performance. So, how …

In the field of modern computing technology, computing chips play a central role in enabling edge computing and artificial intelligence. However, different chip types, such as GPUs and ASICs, have significant differences in performance, energy efficiency, and applicable scenarios. Choosing the right computing chip is crucial for meeting specific application needs, optimizing performance, and reducing operational costs. This article explores …

As artificial intelligence (AI) technologies advance and the demand for edge computing grows, traditional general-purpose processors like CPUs can no longer meet the performance needs of high-intensity AI tasks. To further enhance real-time capabilities, computational efficiency, and energy consumption, specialized hardware such as GPUs, TPUs, and FPGAs are being increasingly integrated into edge computing devices. These hardware components not only …

As edge computing becomes increasingly prevalent in fields like artificial intelligence (AI), the Internet of Things (IoT), and industrial automation, hardware architecture has emerged as a key factor in determining the performance of edge devices. Different hardware architectures offer unique advantages in terms of performance, energy efficiency, and scalability, making the choice of architecture critical for meeting specific application requirements. …

When evaluating the performance of edge computing devices—particularly edge computing boxes—computing power is undoubtedly a fundamental parameter. However, focusing solely on computing power is insufficient to fully assess their real-world performance. As edge computing becomes increasingly prevalent in industries like manufacturing, transportation, healthcare, and smart cities, a holistic performance evaluation is critical. In this article, we explore key performance indicators …

In the world of edge computing devices, computational power—measured as FLOPS (Floating Point Operations Per Second)—is often regarded as the central benchmark for performance evaluation. But is it sufficient to focus solely on FLOPS? Are there other metrics that are equally important, or even more critical in specific scenarios? In this article, we explore the role of computational power and …