Deep neural network (DNN)-based methodologies have shown impressive performance and robustness in the detection of abnormalities and decoding of multi-modal biosignals. While the use of DNNs provides promising classification and decoding capabilities, it also introduces significant design and cost challenges for the implementation of biomedical System on Chips (SoC). To address the increasing demand for advanced and efficient DNN-based healthcare solutions at the edge, we propose BioSeek, an agile design generation framework enhanced by cutting-edge large-language models (LLM). BioSeek offers a comprehensive solution to the design challenges associated with biosignal processors. The effectiveness of BioSeek is evaluated through the design generation of both application-specific and versatile biosignal processors, demonstrating performance that is competitive with existing solutions.

Kilometer-scale Earth system models (ESMs) necessitate exascale supercomputers to facilitate realistic simulations of weather phenomena and climate variability over a time span ranging from days to decades. We present AP3ESM, an ultra‑high‑resolution, AI‑Powered, Performance‑Portable ESM coupling atmosphere, land surface, ocean, and sea ice components. By leveraging the performance portability features of Kokkos and OpenMP, the AP3ESM operates efficiently on two heterogeneous systems while incurring minimal development overhead. Advanced optimization techniques, such as adaptive parallel algorithms, AI-enhanced physical parameterizations, and mixed-precision computations, have been implemented to further boost the computational efficiency. Breaking the 1-km resolution barrier, AP3ESM delivers 0.85 and 1.98 simulated-years-per-day (SYPD) for the standalone atmosphere and ocean components on 34.1 million Sunway cores and 16085 GPUs, respectively; the holistic AP3ESM achieves 0.54 SYPD on 37.2 million Sunway cores. Notably, the forecast experiment successfully captures Super Typhoon Doksuri in 2023 and its associated extreme rainfall across China.