NXMind, a company within Dreame’s ecosystem, officially unveiled its Tianqiong chip series on March 11. It said the chips have entered mass production and will soon be deployed across Dreame’s robotics product lineup.
Demand for computing power is expanding at a pace that exceeds the historical trajectory of Moore’s Law amid the artificial intelligence boom. According to a 2018 analysis by OpenAI, compute used in the largest AI training runs has doubled every 3.4 months since 2012, increasing by more than 300,000-fold over that period. As large models continue to scale in parameter size, and edge intelligence evolves from basic perception to more complex decision-making, computing power has emerged as a central constraint in the next phase of AI competition.
At the same time, global computing supply faces structural bottlenecks. Developers are approaching the physical limits associated with Moore’s Law, slowing improvements in transistor density. Capacity for advanced semiconductor manufacturing processes also remains constrained, creating uncertainty in chip supply. In addition, terrestrial data centers face limits related to energy consumption targets, cooling efficiency, and land availability, making it difficult to sustain the pace at which AI computing demand is expanding.
According to a 2024 report by the International Energy Agency, global data center electricity consumption could exceed 1,000 terawatt-hours by 2026, roughly equivalent to Japan’s annual electricity usage. Rising energy demand, cooling constraints, and site availability have become three major factors limiting the expansion of computing infrastructure.
As a result, competition in the semiconductor sector is undergoing a structural shift. Edge chips are evolving from single-purpose components into system-level intelligent hubs, with highly integrated system-on-chip designs becoming key enablers of embodied intelligence. At the same time, the physical deployment of computing infrastructure is expanding beyond Earth’s surface toward architectures that integrate space, air, and ground networks. Computing nodes in low Earth orbit (LEO) are emerging as a new frontier, with global technology companies exploring deployments in this domain.
Wu Zhongze, former vice minister of China’s Ministry of Science and Technology, said at this year’s Appliance & Electronics World Expo that chipmakers are quietly reshaping how the world operates. In recent years, China has promoted deeper integration across innovation chains, industrial supply chains, capital networks, and talent pipelines within the chip and computing sectors to support long-term industry development.
From a broader perspective, the rise of edge intelligence is redefining chip design paradigms. As embodied intelligence and humanoid robots move from laboratory research toward industrial deployment, chips are no longer just computing components. Instead, they serve as the backbone connecting perception, understanding, decision-making, and execution.
Robots operating in complex and dynamic environments must respond in real time, placing strict requirements on latency, power consumption, and reliability. Traditional general-purpose chip architectures often struggle to balance performance and efficiency under these conditions. This has accelerated the development of specialized system-on-chip architectures, where heterogeneous computing units integrate perception fusion, decision planning, and motion control within a single chip. The result is an edge intelligence system capable of operating locally on the device.
NXMind’s Tianqiong chip series reflects this shift. The chip features a heterogeneous computing platform composed of a multi-core CPU, a dedicated neural processing unit, and an independent microcontroller unit.
NXMind said the chip will be deployed by Dreame to support functions such as LiDAR (light detection and ranging), AI-driven vision fusion, and binocular obstacle avoidance algorithms. These capabilities are intended to improve navigation and obstacle avoidance in complex home environments.
The company’s differentiation strategy builds on Dreame’s existing technology base. Rather than developing a chip platform independently from the ground up, NXMind draws on Dreame’s accumulated algorithm expertise, supply chain ecosystem, and operational data generated from product shipments. According to the company, this enables a collaborative design approach that links chip architecture with algorithms.
Scenario-defined chips have increasingly become a strategy for semiconductor companies seeking to establish competitive barriers.
Meanwhile, the structure of computing infrastructure is evolving. As ground-based data centers encounter physical and environmental constraints, space-based computing deployments are being explored as an alternative approach.
LEO provides a vacuum environment that naturally supports heat dissipation, along with continuous access to solar energy. In theory, this could enable higher-density computing deployments with lower cooling costs. Space-based computing nodes could also provide onboard processing for global LEO satellite internet systems, space-based remote sensing data, and deep space exploration missions, reducing delays associated with transmitting data back to ground stations.
According to 36Kr, NXMind’s first Yaotai space computing module is scheduled to launch into orbit in March. The launch is intended to begin construction of a supercomputing center in LEO and initiate preliminary verification of an in-orbit computing network. The move also suggests that NXMind is transitioning from an R&D phase toward early commercialization.
NXMind’s current business portfolio spans smartphone processors, autonomous driving chips, robotics chips, space-based computing technology, and personal AI computers. Together, these initiatives form a broad product ecosystem.
This trajectory reflects a wider shift across the AI sector. Computing power is expanding from centralized cloud infrastructure toward distributed edge environments, while also extending into integrated architectures that connect space, air, and ground systems.
KrASIA features translated and adapted content that was originally published by 36Kr. This article was written by Huang Nan for 36Kr.