19 citations · 51 across the 7 of their papers we have counts for
8 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…
Decoupled Control Flow and Data Access in RISC-V GPGPUs
Giuseppe M. Sarda, Nimish Shah, Abubakr Nada +2
Vortex, a newly proposed open-source GPGPU platform based on the RISC-V ISA, offers a valid alternative for GPGPU research over the broadly-used modeling platforms based on commerc…
Optimising GPGPU Execution Through Runtime Micro-Architecture Parameter Analysis
Giuseppe M. Sarda, Nimish Shah, Debjyoti Bhattacharjee +2
GPGPU execution analysis has always been tied to closed-source, proprietary benchmarking tools that provide high-level, non-exhaustive, and/or statistical information, preventing a…
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…
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…