19 citations · 57 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
GRAPHOPT: constrained-optimization-based parallelization of irregular graphs
Nimish Shah, Wannes Meert, Marian Verhelst
Sparse, irregular graphs show up in various applications like linear algebra, machine learning, engineering simulations, robotic control, etc. These graphs have a high degree of pa…
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…