Nimish Shah

According to our database, Nimish Shah authored at least 20 papers between 2004 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Optimising GPGPU Execution Through Runtime Micro-Architecture Parameter Analysis.

Giuseppe Maria Sarda, Nimish Shah, Debjyoti Bhattacharjee, Peter Debacker, Marian Verhelst

Proceedings of the IEEE International Symposium on Workload Characterization, 2023

2022

GraphOpt: Constrained-Optimization-Based Parallelization of Irregular Graphs.

Nimish Shah, Wannes Meert, Marian Verhelst

IEEE Trans. Parallel Distributed Syst., 2022

DPU: DAG Processing Unit for Irregular Graphs With Precision-Scalable Posit Arithmetic in 28 nm.

Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst

IEEE J. Solid State Circuits, 2022

DPU-v2: Energy-efficient execution of irregular directed acyclic graphs.

Nimish Shah, Wannes Meert, Marian Verhelst

Proceedings of the 55th IEEE/ACM International Symposium on Microarchitecture, 2022

Discrete Samplers for Approximate Inference in Probabilistic Machine Learning.

Shirui Zhao, Nimish Shah, Wannes Meert, Marian Verhelst

Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition, 2022

2021

DPU: DAG Processing Unit for Irregular Graphs with Precision-Scalable Posit Arithmetic in 28nm.

Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst

CoRR, 2021

9.4 PIU: A 248GOPS/W Stream-Based Processor for Irregular Probabilistic Inference Networks Using Precision-Scalable Posit Arithmetic in 28nm.

Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst

Proceedings of the IEEE International Solid-State Circuits Conference, 2021

2020

Dynamic Complexity Tuning for Hardware-Aware Probabilistic Circuits.

Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst

Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020

Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams.

Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst

Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

Acceleration of probabilistic reasoning through custom processor architecture.

Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst

Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

2019

Towards Hardware-Aware Tractable Learning of Probabilistic Models.

Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications.

Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst

Proceedings of the Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing, 2019

ProbLP: A framework for low-precision probabilistic inference.

Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst

Proceedings of the 56th Annual Design Automation Conference 2019, 2019

2018

Runtime Programmable and Memory Bandwidth Optimized FPGA-Based Coprocessor for Deep Convolutional Neural Network.

Nimish Shah, Paragkumar Chaudhari, Kuruvilla Varghese

IEEE Trans. Neural Networks Learn. Syst., 2018

2012

Scalable hierarchical floorplanning for fast physical prototyping of systems-on-chip.

Renshen Wang, Nimish Shah

Proceedings of the International Symposium on Physical Design, 2012

2009

Facial Features Classification Using the Temporal Correlation Matrix Memory (TCMML).

Nimish Shah

Proceedings of the First International Conference of Soft Computing and Pattern Recognition, 2009

2007

The Improved Correlation Matrix Memory (CMML).

Nimish Shah, Simon O'Keefe, Jim Austin

Proceedings of the International Joint Conference on Neural Networks, 2007

2005

"Rippling: Meta-Level Guidance for Mathematical Reasoning, " by Alan Bundy, David Basin, Dieter Hutter, and Andrew Ireland, Cambridge University Press, 2005.

Nimish Shah

J. Autom. Reason., 2005

2004

Program Construction: Calculating Implementations from Specifications by R.C. Backhouse, John Wiley & Sons, 2004.

Nimish Shah

J. Funct. Program., 2004

Knowledge Representation, Reasoning and Declarative Problem Solving by C. Baral, Cambridge University Press, 2003.

Nimish Shah

J. Funct. Program., 2004