In-store Computing Technical Notes (Internet Services - Other Internet Services)
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In-store Computing Technical Notes | |
1. In-store computing has a wide range of application scenarios at the cloud edge Based on their different device characteristics and computing methods, in-store computing products can provide a wide range of AI capabilities such as inference and training for cloud-side applications, improving computing efficiency, reducing system power consumption and equipment costs. 1.1 End-side application scenarios According to IDC, the number of IoT devices worldwide will exceed 40 billion in 2025, generating. The amount of data is close to 80ZB[10],in many scenarios such as smart city, smart home, autonomous driving, etc. More than half of the data needs to be processed locally by the terminal, with a single device computing power requirement of about 0.1 to 64 TOPS. Between In addition, various types of terminal devices have high requirements for runtime, power consumption, portability, etc. For example, smart glasses/headphones need to guarantee a full load standby time of more than 16 hours and the maximum operating power consumption of mobile phones. Future development of end-side devices will focus more on latency, power consumption, cost and privacy, etc. demand characteristics, as shown in Figure 1. 图片1.png 1.2 Side-by-side use scenarios With the rapid rise of edge computing applications such as cloud gaming and Telematics, massive amounts of data will be processed at the edge side and the traffic model will gradually expand from the cloud side to the edge side. The demand for single device arithmetic power in edge computing scenarios is around 64~256TOPS, with high latency requirements, such as end-to-end latency required for smart ports. In addition, the end-to-end delay is 3~100ms due to the edge-side devices. They are usually deployed in locations such as near data production or use, and have high heat dissipation requirements. Overall. The future development of edge-side devices will focus more on demand characteristics such as latency, power consumption, cost and versatility. As shown in Figure 1-2. 图片2.png Compared with traditional solutions, deposit and computation in one has unique advantages in areas such as deep learning and can be compared to traditional solutions, deposit and computation in one has unique advantages in areas such as deep learning and can provide. The computing efficiency ratio is tens of times higher than conventional devices, and the in-store computing chip can provide, through architectural innovation. The comprehensive performance of the chip and board is expected to have a wide range of applications in edge-side reasoning scenarios. Servicing a wide range of Edge Al operations. 1.3 Cloud-side application scenarios Unstructured data, mainly images, voice and video, is growing at a high rate. According to IDC forecasts, the demand for intelligent computing power will increase 500 times by 2030, and intelligent computing centres with AI computing power as the core will become the mainstream of computing power infrastructure, with large-scale intensive construction of AI chips bringing high power consumption challenges. Smart computing centres call for new AI chips to meet the characteristics of large computing power, high bandwidth and low power consumption on the cloud side, as shown in Figure 1-3. 图片3.png As a key next-generation AI chip technology for smart computing centres, in-store computing is evolving towards high computing power, versatility and high computing accuracy, and is expected to provide green and energy-efficient large-scale AI computing power for smart computing centres. 2. Five technical challenges in in-store computing The generalised storage computing technology is gradually moving from academic research to commercial application, with near-storage computing and in-storage processing facing high manufacturing and packaging technology thresholds in the product implementation phase. In-deposit computing is less mature and needs to be strengthened in many aspects, from device development and manufacturing, circuit design, chip architecture, EDA tool chain to software and algorithm ecology, which requires closer collaboration among all segments of the industry chain. | |
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2024-05-03 (0.222 sec)