Alberto Marchisio

DeepAI AI Chat

Featured Co-authors

Muhammad Shafique
112 publications
Muhammad Abdullah Hanif
45 publications
Maurizio Martina
21 publications
Semeen Rehman
20 publications
Faiq Khalid
19 publications
Ihsen Alouani
17 publications
Vojtech Mrazek
17 publications
Rachmad Vidya Wicaksana Putra
16 publications
Guido Masera
12 publications
Amira Guesmi
12 publications
Beatrice Bussolino
7 publications

research

∙ 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. ∙

research

∙ 04/08/2023

SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers

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

0 Alberto Marchisio, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 04/18/2022

Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems

The real-world use cases of Machine Learning (ML) have exploded over the...

22 Shail Dave, et al. ∙

research

∙ 09/20/2021

Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework

The security and privacy concerns along with the amount of data that is ...

7 Muhammad Shafique, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 12/09/2020

Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters

Deep Learning (DL) algorithms have gained popularity owing to their prac...

14 Rida El-Allami, et al. ∙

research

∙ 12/09/2020

MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences

Embedded systems have proliferated in various consumer and industrial ap...

0 Alessio Colucci, et al. ∙

research

∙ 10/12/2020

DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware

Deep Neural Networks (DNNs) have been established as the state-of-the-ar...

0 Alberto Marchisio, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 12/02/2019

ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations

Recent advances in Capsule Networks (CapsNets) have shown their superior...

0 Alberto Marchisio, et al. ∙

research

∙ 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. ∙

research

∙ 02/04/2019

CapStore: Energy-Efficient Design and Management of the On-Chip Memory for CapsuleNet Inference Accelerators

Deep Neural Networks (DNNs) have been established as the state-of-the-ar...

0 Alberto Marchisio, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 11/02/2018

CapsAcc: An Efficient Hardware Accelerator for CapsuleNets with Data Reuse

Deep Neural Networks (DNNs) have been widely deployed for many Machine L...

0 Alberto Marchisio, et al. ∙

research

∙ 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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