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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...
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04/08/2023
SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers
Transformers' compute-intensive operations pose enormous challenges for ...
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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...
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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...
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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...
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08/03/2022
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor
Autonomous Driving (AD) related features represent important elements fo...
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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...
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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...
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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...
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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...
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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 ...
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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...
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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...
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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...
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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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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...
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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...
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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...
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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 ...
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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...
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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...
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04/15/2020
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks
Capsule Networks (CapsNets), recently proposed by the Google Brain team,...
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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...
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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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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...
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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...
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01/28/2019
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks
Capsule Networks envision an innovative point of view about the represen...
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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...
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10/27/2018
