Nanyang Technological University, Singapore (NTU Singapore) and computing technology company Canaan Inc. have launched a joint research initiative valued at S$7 million to advance green intelligent computing technologies and edge artificial intelligence. The partnership, formally designated as the NTU-Canaan Green Intelligent Compute (GIC) Collaboration, is designed to address the rising global demand for computing resources driven by rapid advancements in AI. As model sizes expand and industry applications grow, the initiative focuses on balancing operational capacity with power efficiency and environmental sustainability over a four-year period.

The collaboration is hosted by NTU’s Centre in Computational Technologies for Finance (CCTF) and pairs Canaan’s industrial track record in application-specific integrated circuit (ASIC) design with NTU’s academic research capabilities in AI, computer engineering, and data science. Canaan will supply hardware platforms, engineering expertise, computing infrastructure, and practical deployment scenarios, while NTU researchers will provide scientific leadership and academic testbeds. This structural approach establishes a direct feedback loop between university research and industrial engineering, aiming to translate scientific breakthroughs into viable industrial solutions.

The research programme is organized around four core directions intended to connect algorithm development with real-world system deployment. The first area focuses on green and energy-aware computing, where the joint team will investigate energy prediction, dynamic resource management, and scheduling for energy-intensive workloads. Drawing on Canaan’s operational computing expertise, researchers will explore how tasks can adapt to grid energy availability, system loads, and task priorities to reduce the environmental footprint of AI operations.

The second research direction addresses privacy-preserving edge AI for smart retail environments. Retail settings generate continuous camera and sensor data feeds, which traditionally require transmission to central cloud servers. Instead, the team will develop local edge inference and data minimisation methods that allow devices to process video and sensor data locally. These solutions are aligned with Singapore’s Personal Data Protection Act (PDPA), aiming to cut network strain and strengthen data privacy by keeping sensitive information on local devices rather than in the cloud.

The third focus area involves cloud-edge-end collaborative inference. Recognizing that purely cloud-based or device-based models cannot meet every operational requirement, the team will examine model partitioning and workload allocation. This approach allows complex AI tasks to run across cloud servers, edge gateways, and endpoint devices, creating a more adaptive and efficient system architecture. By distributing computational loads, the project aims to make AI workloads more energy-aware, secure, and ready for deployment in diverse real-world environments.

The fourth direction centers on edge-AI education and curriculum development. The collaboration will translate research outcomes into educational software development kits, laboratory modules, and teaching materials. These tools will be incorporated into NTU’s academic curriculum to train students in chip architectures, system constraints, and software deployment. This educational component is designed to equip talent with the specific skills needed to meet future technological demands, strengthening Singapore’s position as a hub for sustainable digital innovation.

Professor Shirley Meng, Vice President (Industry) at NTU Singapore, stated that managing the environmental footprint of AI has become a critical global challenge. She noted that the partnership reflects NTU’s commitment to translating scientific breakthroughs into viable industrial solutions. By co-developing energy-efficient edge computing systems and establishing joint training modules, the university aims to strengthen Singapore’s position as a hub for sustainable digital innovation and equip its talent to meet future technological demands.

Nangeng Zhang, Founder, Chairman, and CEO of Canaan, emphasized that computing efficiency is becoming a system-level challenge spanning chips, software, workload scheduling, and energy use. He described the collaboration as an effort to bring together academic research and Canaan’s engineering experience to explore how computing systems can become more energy-efficient, adaptive, and practical for deployment. Zhang also viewed the partnership as part of Canaan’s long-term commitment to Singapore’s research and technology ecosystem, highlighting the importance of bridging algorithmic design and physical hardware.

The initiative aims to support four PhD researchers and generate approximately 10 academic papers over its four-year course. The joint team also plans to produce four technical work-package reports, develop functional industrial prototypes, and file patents where commercially applicable. The collaboration aligns with Singapore’s national priorities for sustainable digital infrastructure and AI development, supporting frameworks such as the Green Data Centre Roadmap and Singapore Standard SS 715:2025, which prioritize energy efficiency across digital facilities and IT equipment.

Subject of Research: Joint research initiative for green intelligent computing and edge AI

Article Title: NTU Singapore and Canaan partner in S$7 million initiative to advance sustainable computing and edge AI

Article References: NTU Singapore and Canaan partner in S$7 million initiative to advance sustainable computing and edge AI. Original publication (Bibliographic details unavailable.)

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Keywords: green intelligent computing, edge artificial intelligence, NTU Singapore, Canaan Inc, energy-aware algorithms, privacy-preserving edge systems, smart retail, ASIC design, cloud-edge-end inference, sustainable digital infrastructure, Singapore PDPA, computing efficiency, AI workload scheduling, edge AI education, Green Data Centre Roadmap, SS 715:2025, joint research initiative, data minimisation, local edge inference, S$7 million funding