2 citations · 2 across the 3 of their papers we have counts for
4 papers
EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model
S M Mahbub Murshed, Hayden Bright, Rezaul Karim +1
Deploying language models (LMs) on the edge presents unique memory, latency, and power challenges absent in high-performance computing. KV-cache compression emerges as a promising…
AQVolt26: High-Temperature rSCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
Jiyoon Kim, Chuhong Wang, Aayush R. Singh +6
The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobility, electrochemical stability…
AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis
Omar Allam, Brook Wander, SungYeon Kim +16
Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some lim…
Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers
Tyler Sours, Shivang Agarwal, Marc Cormier +7
Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing pro…