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∙ 10/11/2022

RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

Neural Architecture Search (NAS) algorithms aim at finding efficient Dee...

0 Alberto Marchisio, et al. ∙

∙ 09/26/2022

Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile

Most of today's computer vision pipelines are built around deep neural n...

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∙ 06/21/2022

Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations

Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit...

0 Alberto Marchisio, et al. ∙

∙ 12/21/2020

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life...

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∙ 08/19/2020

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach ...

0 Alberto Marchisio, et al. ∙

∙ 04/15/2020

Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks

Capsule Networks (CapsNets), recently proposed by the Google Brain team,...

0 Alberto Marchisio, et al. ∙

∙ 05/24/2019

X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations

Convolutional Neural Networks (CNNs) are extensively in use due to their...

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