The paper introduces an integrated platform combining BIM, sensor data, and advanced algorithms to significantly optimize energy consumption in green building design, achieving a 29.3% reduction in en…

The paper introduces PIRS, a physics-informed reward shaping method that replaces ad-hoc comfort proxies with the ISO 7730 PMV formulation, enabling deep reinforcement learning agents to achieve energ…

This paper presents BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge.

The paper introduces Chimera, a highly efficient and scalable MCU designed for ultra-low-power edge AI inference, achieving 3.1 TOPS/W by integrating a dedicated transformer accelerator and a QoS-guar…

This paper introduces a high-fidelity, integrated hardware-software simulator for LPDDR5X-PIM, enabling precise evaluation of system performance and energy efficiency.

This paper investigates the potential of real-world Processing-in-Memory (PIM) architectures, specifically using UPMEM, to accelerate cryptographic algorithms, demonstrating that distributing computat…

The paper presents an IoT-enabled smart home system using Raspberry Pi 5 and environmental sensors to automatically manage devices, achieving over 46% energy savings compared to always-on models.

The paper proposes EnThM, a lightweight, hierarchical verification scheme that uses statistical and rule-based checks on aggregated metering data to mitigate real-time power theft in smart grids.

The paper models how AI-driven data center demand stresses the electrical grid, finding that relying solely on renewable energy certificates (RECs) is insufficient and that on-site storage and spatial…

This paper proposes an Explainable Deep Reinforcement Learning (XRL) framework to optimize energy management in complex buildings, demonstrating that on-policy algorithms provide superior cost reducti…

EnergyMamba proposes an uncertainty-aware, graph-enhanced selective state space model to significantly improve both the accuracy and reliability of energy consumption prediction by explicitly modeling…

The paper proposes an uncertainty-aware transfer learning framework using the Temporal Fusion Transformer (TFT) to achieve robust and scalable energy forecasting across different buildings, demonstrat…

This paper investigates the thermal constraints of deploying AI compute infrastructure in space, comparing GPUs and compute-in-memory (CIM) accelerators using a co-design methodology.

This paper systematically identifies long-term operational risks associated with smart household appliances, using the smart fridge as a case study, and finds that even basic functions are vulnerable…

The paper introduces 'quantum-safe,' a Python library that addresses the remaining 'production gap' in post-quantum cryptography (PQC) by providing robust, easy-to-use hybrid implementations and compr…

OccuReward introduces an LLM-guided framework and a Comfort Equity Index (CEI) to shape building energy rewards, demonstrating that iterative refinement significantly improves occupant comfort equity…

TIMEGATE introduces a resource-aware policy layer that manages continual ML adaptation by dynamically budgeting time and evaluation resources, achieving significant compute and energy savings without…

The paper proposes Multi-Adapter Representation Interventions via Energy Calibration (MARI), a method that adaptively adjusts the strength and direction of interventions across different inputs to imp…

The paper introduces SMH-Bench, a comprehensive benchmark built on a simulator to rigorously test LLM agents' ability to perform complex, environment-grounded reasoning and actions in realistic smart-…

The paper proposes an energy-efficient drag reduction strategy for turbulent flows by combining Multi-Agent Deep Reinforcement Learning with SHAP-guided explainable deep learning, achieving superior p…