TII · · Significant research
The Technology Innovation Institute (TII) in Abu Dhabi, in collaboration with NVIDIA, has demonstrated large-scale simulations of the adiabatic quantum annealing (QA) algorithm for problem instances involving up to 500,000 qubits. TII's simulator achieved solution quality exceeding that of all solvers evaluated from the MQLib repository, a library for combinatorial optimization benchmarking. The emulator is accessible to external users via an experimental cloud platform hosted at https://q-inspired.tii.ae. Why it matters: This collaboration expands the range of complex optimization problems that can be investigated using quantum-inspired approaches, beyond those currently achievable with near-term quantum hardware.
TII · NVIDIA · quantum annealing · qubits · simulation
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arXiv ·
Team TII EuroRacing (TII-ER) developed a full autonomous software stack for oval racing, enabling speeds above 75 m/s (270 km/h). The software includes modules for perception, planning, control, vehicle dynamics modeling, simulation, telemetry, and safety. The team achieved second and third place in the first two Indy Autonomous Challenge events using this stack.
MBZUAI ·
G42 and Cerebras, in partnership with MBZUAI and C-DAC, will deploy an 8 exaflop AI supercomputer in India. The system will operate under India's governance frameworks, with all data remaining within national jurisdiction to meet sovereign security and compliance requirements. The supercomputer will be accessible to Indian researchers, startups, and government entities under the India AI Mission.
MBZUAI ·
Researchers in Abu Dhabi developed H-SURF, a swarm of bio-inspired robotic fish for underwater data collection. Funded by the Technology Innovation Institute (TII) and conducted at Khalifa University, H-SURF uses swarm intelligence and optical communication to minimize disturbance to marine life. The project was recently recognized with the Sheikh Hamdan bin Zayed Award for Environmental Research.
arXiv ·
This paper analyzes the energy consumption and carbon footprint of LLM inference in the UAE compared to Iceland, Germany, and the USA. The study uses DeepSeek Coder 1.3B and the HumanEval dataset to evaluate code generation. It provides a comparative analysis of geographical trade-offs for climate-aware AI deployment, specifically addressing the challenges and potential of datacenters in desert regions.