Time-dependent reliability analysis of nonlinear dynamical systems under stochastic excitations is a critical yet computationally demanding task. Conventional approaches, such as Monte Carlo simulation, necessitate repeated evaluations of computationally expensive numerical solvers, leading to significant computational bottlenecks. To address this challenge, we propose \textit{CoNBONet}, a neuroscience-inspired surrogate model that enables fast, energy-efficient, and uncertainty-aware reliability analysis, providing a scalable alternative to techniques such as Monte Carlo simulations. CoNBONet, short for \textbf{Co}nformalized \textbf{N}euroscience-inspired \textbf{B}ayesian \textbf{O}perator \textbf{Net}work, leverages the expressive power of deep operator networks while integrating neuroscience-inspired neuron models to achieve fast, low-power inference. Unlike traditional surrogates such as Gaussian processes, polynomial chaos expansions, or support vector regression, that may face scalability challenges for high-dimensional, time-dependent reliability problems, CoNBONet offers \textit{fast and energy-efficient inference} enabled by a neuroscience-inspired network architecture, \textit{calibrated uncertainty quantification with theoretical guarantees} via split conformal prediction, and \textit{strong generalization capability} through an operator-learning paradigm that maps input functions to system response trajectories. Validation of the proposed CoNBONet for various nonlinear dynamical systems demonstrates that CoNBONet preserves predictive fidelity, and achieves reliable coverage of failure probabilities, making it a powerful tool for robust and scalable reliability analysis in engineering design.

Event-driven spiking neural networks(SNNs) are promising neural networks that reduce the energy consumption of continuously growing AI models. Recently, keeping pace with the development of transformers, transformer-based SNNs were presented.

As AI drives extraordinary power demands, energy intelligence is rapidly becoming a core business metric. Loudoun County, Virginia, once known for its pastoral scenery and proximity to Washington, DC, has earned a more modern reputation in recent years: The area has the highest concentration of data centers on the planet. Ten years ago, these facilities powered email and e-commerce. Today, thanks to the meteoric rise in demand for AI-infused everything, local utility Dominion Energy is working hard to keep pace with surging power demands. The pressure is so acute that Dulles International Airport is constructing the largest airport solar installation in the country, a highly visible bid to bolster the region's power mix. Data center campuses like Loudoun's are cropping up across the country to accommodate an insatiable appetite for AI.

Energy-efficient computing is of primary importance to the effective deployment of deep neural networks (DNNs), particularly in edge devices and in on-chip AI systems. Increasing DNN computation's energy efficiency and lowering its carbon footprint require iterative efforts from both chip designers and algorithm developers.

Our datasets and code are available via the following links: Github: https://github.com/NREL/BuildingsBench As described in Sec. 3 and Sec. 4, Buildings-900K and the BuildingsBench benchmark datasets are B.1 Motivation Q: For what purpose was the dataset created? It specifically addresses a lack of appropriately sized and diverse datasets for pretraining STLF models. We emphasize that the EULP was not originally developed for studying STLF. Rather, it was developed as a general resource to "...help electric utilities, grid operators, manufacturers, Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity Q: Who funded the creation of the dataset?

Short-term load forecasting (STLF), which typically ranges from hour-ahead to a few days ahead, plays a multitude of critical roles.