2 papers
cs.AR2026
Design Rules for Extreme-Edge Scientific Computing on AI Engines
Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos +3
Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and throughput requirements demand sma…
cs.LG2025
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
Tommaso Baldi, Javier Campos, Olivia Weng +4
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensin…