In mid-October, the World Computing Power Network Conference and GOSIM Shenzhen 2026 were held back-to-back in Shenzhen, with edge computing power and cloud-edge collaboration becoming the shared agenda of both events. From October 15 to 16, the World Computing Power Network Conference convened in Nanshan, Shenzhen, under the theme “Converged Computing Network, Super-Intelligent Fusion,” with topics covering distributed edge computing-power networks. Shen Xuemin, a foreign member of the Chinese Academy of Engineering, delivered a report titled “Edge Computing Power Networks for Low-Latency Embodied Applications,” proposing that AI at the edge pushes inference and control down to the side closer to the data source. Immediately afterward, GOSIM Shenzhen 2026 ran from October 16 to 17, whose edge-agent AI agenda focused on the fusion of edge inference and cloud inference, using warehouse safety monitoring as an example to discuss the division of tasks between the edge side and the cloud.
Looking at the technical consensus conveyed by the two conferences, AI inference is moving from data centers to the industrial field: where a model is deployed must be decided by business conditions — response latency, network connectivity, and data-processing requirements all shape the deployment approach. Some tasks can be completed on the device or at a nearby edge node, while the rest are handled in coordination with the cloud. This brings very real hardware-selection constraints: besides providing computing power, devices on the factory floor must also store system programs, model files, and operational records. In environments with temperature swings, vibration, or power fluctuations, the operating temperature range of storage components, power-loss protection, hardware encryption, write protection, and burn-in validation of the whole machine all become evaluation criteria.
From the perspective of the industry landscape, the key change in this round is that “cloud-edge collaboration” is moving from concept to an engineering-grade division of labor. The industrial signal conveyed by the conferences is that the environmental adaptability and data-protection capability of supporting hardware — storage components in particular — are drawing industry-chain attention, which means edge-side competition is no longer just a stack-up of TOPS, but a system-level contest of “computing power plus storage plus reliability.” The World Computing Power Network Development White Paper, jointly compiled by Pengcheng Laboratory and the World Computing Power Network Alliance, also points out that global computing power is accelerating toward intelligence and clustering while facing uneven resource distribution — edge computing-power networks are precisely the technical answer to balancing that distribution. As AI capability spreads from the machine room to the field, whoever can reliably deliver inference and control under wide-temperature, vibration-resistant, and unattended conditions will take the lead in industrial AI adoption.
For Junyi Microelectronics, the engineering-grade landing of cloud-edge collaboration is exactly where the company’s product lines come into play. Junyi’s edge-computing hosts 5G-EDGE-3399Pro/AI-BOX-3588 are positioned as nearby inference and control nodes for industrial AI inspection, predictive maintenance, and similar scenarios, directly corresponding to the technical route of “pushing inference and control down to the data-source side.” The core boards J-3568/J-3576/J-3588 and K-series edge-intelligent motherboards provide a standardized embedded computing foundation for devices and edge nodes, while the industrial mini PC IPC-i5-1235U and touch-panel all-in-one machines serve on-site human-machine interaction and data acquisition. The emphasis both conferences placed on the environmental adaptability of field hardware also validates the direction of Junyi’s long-term investment in wide-temperature design, burn-in validation, and industrial reliability.
Source: https://baijiahao.baidu.com/s?id=1880285508169654702 (2026-10-28)