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08/22/2023
Automatic Task Parallelization of Dataflow Graphs in ML/DL models
Several methods exist today to accelerate Machine Learning(ML) or Deep-L...
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12/06/2022
Model-Based and Model-Free point prediction algorithms for locally stationary random fields
The Model-free Prediction Principle has been successfully applied to gen...
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11/06/2022
Effective City Planning: A Data Driven Analysis of Infrastructure and Citizen Feedback in Bangalore
Leveraging civic data, divided into 3 categories spending, infrastructur...
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10/15/2021
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
Quantization and pruning are core techniques used to reduce the inferenc...
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10/06/2021
Tuning Confidence Bound for Stochastic Bandits with Bandit Distance
We propose a novel modification of the standard upper confidence bound (...
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09/29/2021
Kernel distance measures for time series, random fields and other structured data
This paper introduces kdiff, a novel kernel-based measure for estimating...
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07/15/2021
An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling
A novel energy-efficient edge computing paradigm is proposed for real-ti...
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01/31/2021
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
The use of Deep Learning hardware algorithms for embedded applications i...
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01/30/2021
A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?
When trained as generative models, Deep Learning algorithms have shown e...
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10/28/2019
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Stochastic-sampling-based Generative Neural Networks, such as Restricted...
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03/11/2019
AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks
The power budget for embedded hardware implementations of Deep Learning ...
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12/06/2017
Predictive inference for locally stationary time series with an application to climate data
The Model-free Prediction Principle of Politis (2015) has been successfu...
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05/07/2017
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
In recent years deep learning algorithms have shown extremely high perfo...
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04/13/2017
ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks
Probabilistic generative neural networks are useful for many application...
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02/18/2016
A Nonparametric Framework for Quantifying Generative Inference on Neuromorphic Systems
Restricted Boltzmann Machines and Deep Belief Networks have been success...
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09/24/2015
Mapping Generative Models onto a Network of Digital Spiking Neurons
Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) ...
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03/26/2015
Gibbs Sampling with Low-Power Spiking Digital Neurons
Restricted Boltzmann Machines and Deep Belief Networks have been success...
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11/05/2013