19 citations · 48 across the 4 of their papers we have counts for
4 papers
A Phenomenological AI Foundation Model for Physical Signals
Jaime Lien, Laura I. Galindez Olascoaga, Hasan Dogan +4
The objective of this work is to develop an AI foundation model for physical signals that can generalize across diverse phenomena, domains, applications, and sensing apparatuses. W…
DPU: DAG Processing Unit for Irregular Graphs with Precision-Scalable Posit Arithmetic in 28nm
Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao +2
Computation in several real-world applications like probabilistic machine learning, sparse linear algebra, and robotic navigation, can be modeled as irregular directed acyclic grap…
Acceleration of probabilistic reasoning through custom processor architecture
Nimish Shah, Laura I. Galindez Olascoaga, Wannes Meert +1
Probabilistic reasoning is an essential tool for robust decision-making systems because of its ability to explicitly handle real-world uncertainty, constraints and causal relations…
ProbLP: A framework for low-precision probabilistic inference
Nimish Shah, Laura I. Galindez Olascoaga, Wannes Meert +1
Bayesian reasoning is a powerful mechanism for probabilistic inference in smart edge-devices. During such inferences, a low-precision arithmetic representation can enable improved…