863 citations · 1.1k across the 19 of their papers we have counts for
5 papers · 1 filter
AIA: A 16nm Multicore SoC for Approximate Inference Acceleration Exploiting Non-normalized Knuth-Yao Sampling and Inter-Core Register Sharing
Shirui Zhao, Nimish Shah, Wannes Meert +1
Probabilistic graphical models (PMs) are popular to empower machine learning with the ability of reasoning and decision-making. To perform approximate inference in PMs, sampling-ba…
AIA: A Customized Multi-core RISC-V SoC for Discrete Sampling Workloads in 16 nm
Shirui Zhao, Nimish Shah, Wannes Meert +1
Probabilistic models (PMs) are essential in advancing machine learning capabilities, particularly in safety-critical applications involving reasoning and decision-making. Among the…
DPU-v2: Energy-efficient execution of irregular directed acyclic graphs
Nimish Shah, Wannes Meert, Marian Verhelst
A growing number of applications like probabilistic machine learning, sparse linear algebra, robotic navigation, etc., exhibit irregular data flow computation that can be modeled w…
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…