A research team at Peking University proposed an optoelectronic distributed computing system based on an on-chip all-optical supernode. By integrating high-speed silicon photonic transceiver chips, low-loss optical circuit switching (OCS) chips and multiple computing nodes, the system enables intermediate data between different layers of a deep neural network to be directly transmitted and switched through an on-chip high-speed all-optical supernode. This approach reduces latency overheads associated with conventional data movement, packet scheduling and buffering.

As artificial intelligence expands from cloud computing into robots, autonomous vehicles and other real-world systems, conventional electronic architectures face increasing challenges in latency, bandwidth and energy consumption. Researchers from the University of Shanghai for Science and Technology propose Brain-like Optical Computing (BOC), a new photonic framework integrating sensing, memory and computation for future edge and embodied intelligence.

Professor Minsoo Rhu’s research team at the KAIST School of Electrical Engineering presents the first quantitative analysis of AI agents’ computational cost, response latency, energy consumption, and data-center-scale power demand

AI agents consume up to 136.5 times more energy per query than conventional generative AI, highlighting the need for co-design of data centers and power infrastructure for a sustainable AI era

The novel idea was invented by Giacomo Sasso, a postdoctoral researcher at the School of Engineering and Materials Science at Queen Mary University of London, and it works by transforming invisible forces into dynamic colour patterns. This enables high-resolution maps to contact, strain and pressure to emerge instantly. When pressure is applied to a soft sensing surface, the material produces spatially varying structural colours that can be captured immediately using a standard camera, removing the need for complex reconstruction algorithms. 

Advances in precision oncology necessitate tools capable of effectively imaging and treating deep-seated tumours. Radioactive quantum dots (RQDs) fulfil this requirement by creating self-luminescent probes through the unique fusion of quantum dots’ (QDs) tunable optical properties with the radiological characteristics of radioactive isotopes. This design enables multimodal imaging and synergistic therapy within deep tissues, overcoming the penetration depth limitations of conventional light sources.