On-chip all-optical supernode for ultra-low-latency deep neural network inference
Peer-Reviewed Publication
Updates every hour. Last Updated: 27-Oct-2026 00:16 ET (27-Oct-2026 04:16 GMT/UTC)
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.
Degraded soils and the burgeoning challenge of agricultural waste pose significant hurdles to global food security. A recent study, published in Carbon Research, unveils a promising solution: using solar radiation to enhance hydrochar, a carbon-rich material derived from biomass, to create a more effective soil amendment. This innovative approach offers a low-energy pathway to transform materials like pig manure and mandarin peels into powerful tools for soil remediation.
The research team prepared hydrochars under varying hydrothermal carbonization temperatures (180 °C and 260 °C), then subjected them to simulated solar aging for 200 hours, including periodic wetting. Comprehensive characterization using advanced techniques such as FTIR, XPS, 3D-EEM, and ESI-FT-ICR MS was employed to track changes in the hydrochar's physicochemical properties and the composition of its dissolved organic matter (DOM). Soil microcosm experiments further evaluated the impact of these modified hydrochars on paddy soil for 45 days, assessing changes in soil properties and microbial communities.
Managing agricultural waste presents a dual challenge: controlling harmful ammonia (NH₃) emissions and preventing the spread of toxic metals from manure. Scientists have now devised a sustainable approach that converts coal tailings, a common industrial byproduct, into a highly effective adsorbent. This innovative material, created through a catalytic oxidation process using silver and titanium dioxide (Ag/TiO₂), simultaneously captures ammonia and neutralizes hazardous metals, offering a cost-effective solution for sustainable farming.
A team from The University of Melbourne, Australia, pioneered the method, modifying coal tailings at a modest 250 °C. Unlike conventional high-temperature treatments, this catalytic process is exothermic, generating its own heat, which significantly reduces energy consumption and leads to lower CO₂ emissions. The core material harnesses Ag/TiO₂ to produce reactive oxygen species that functionalize the coal tailings, creating a network of ultramicropores and oxygen-containing chemical groups on its surface.
Microplastics, the persistent pollutants already known to threaten ecosystems and human health, have a previously unrecognized dark side inside the world's urban sewer networks.
In data center networks, the growth rate of switch buffer size is much smaller than that of network bandwidth capacity. This results in frequent network congestion, which requires congestion control. Currently, the classification of end-to-end congestion control protocols is based on single dimensions such as time or congestion arbitration subjects.
A Chinese research team has achieved a breakthrough in improving the training efficiency of Graph Neural Networks (GNNs). They introduced an innovative architecture named "Decentralized Hypercube Collaborative Framework," addressing long-standing challenges such as high memory overhead, low computational efficiency, and underutilized hardware resources in traditional Graph Convolutional Network (GCN) training.
HUST researchers introduce DMCGuard to mitigate security risks in multi-channel IoT management.
Serverless computing typically uses secure containers encapsulated within lightweight microVMs to isolate function invocations across different tenants. However, to achieve high security guarantees, the Single-Container-Per-VM (SCPV) model can lead to significant memory inefficiency, as each microVM includes a separate guest OS, even when the container instances are identical.