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∙ 08/10/2023

A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks

Machine learning (ML) is widely used today, especially through deep neur...

0 Farzad Nikfam, et al. ∙

∙ 04/08/2023

SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers

Transformers' compute-intensive operations pose enormous challenges for ...

0 Alberto Marchisio, et al. ∙

∙ 04/08/2023

RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks

Capsule Networks (CapsNets) are able to hierarchically preserve the pose...

0 Alberto Marchisio, et al. ∙

∙ 10/13/2022

AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient

Adversarial training is exploited to develop a robust Deep Neural Networ...

0 Farzad Nikfam, et al. ∙

∙ 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. ∙

∙ 08/03/2022

LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor

Autonomous Driving (AD) related features represent important elements fo...

0 Alberto Viale, et al. ∙

∙ 07/31/2022

CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks

In today's era of smart cyber-physical systems, Deep Neural Networks (DN...

0 Muhammad Abdullah Hanif, et al. ∙

∙ 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. ∙

∙ 05/27/2022

fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems

Recently, Deep Neural Networks (DNNs) have achieved remarkable performan...

0 Alberto Marchisio, et al. ∙

∙ 09/01/2021

R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors

Spiking Neural Networks (SNNs) aim at providing energy-efficient learnin...

7 Alberto Marchisio, et al. ∙

∙ 07/01/2021

DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks

Spiking Neural Networks (SNNs), despite being energy-efficient when impl...

0 Alberto Marchisio, et al. ∙

∙ 07/01/2021

CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor

Autonomous Driving (AD) related features provide new forms of mobility t...

7 Alberto Viale, 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...

39 Maurizio Capra, et al. ∙

∙ 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. ∙

∙ 05/16/2020

An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processor

Spiking Neural Networks (SNNs), the third generation NNs, have come unde...

0 Riccardo Massa, et al. ∙

∙ 05/16/2020

NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips

Due to their proven efficiency, machine-learning systems are deployed in...

0 Valerio Venceslai, 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...

0 Alberto Marchisio, et al. ∙

∙ 02/04/2019

SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?

Recently, many adversarial examples have emerged for Deep Neural Network...

16 Alberto Marchisio, et al. ∙

∙ 01/28/2019

CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks

Capsule Networks envision an innovative point of view about the represen...

0 Alberto Marchisio, et al. ∙

∙ 10/27/2018

A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks

Activation functions influence behavior and performance of DNNs. Nonline...

0 Alberto Marchisio, et al. ∙

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