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08/11/2023
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
In this paper, we present a comprehensive survey of the current trends f...
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08/06/2023
SAAM: Stealthy Adversarial Attack on Monoculor Depth Estimation
In this paper, we investigate the vulnerability of MDE to adversarial pa...
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07/20/2023
Approximate Computing Survey, Part II: Application-Specific Architectural Approximation Techniques and Applications
The challenging deployment of compute-intensive applications from domain...
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07/20/2023
Approximate Computing Survey, Part I: Terminology and Software Hardware Approximation Techniques
The rapid growth of demanding applications in domains applying multimedi...
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05/21/2023
Reduce: A Framework for Reducing the Overheads of Fault-Aware Retraining
Fault-aware retraining has emerged as a prominent technique for mitigati...
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05/21/2023
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization
Permanent faults induced due to imperfections in the manufacturing proce...
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05/19/2023
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
In this paper, we present a novel approach for generating naturalistic a...
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04/20/2023
eFAT: Improving the Effectiveness of Fault-Aware Training for Mitigating Permanent Faults in DNN Hardware Accelerators
Fault-Aware Training (FAT) has emerged as a highly effective technique f...
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04/08/2023
RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults
To maximize the performance and energy efficiency of Spiking Neural Netw...
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04/08/2023
EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems
Spiking Neural Networks (SNNs) have shown capabilities of achieving high...
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03/24/2023
PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems
Graph neural networks (GNNs) have shown great success in detecting intel...
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03/04/2023
scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation
Approximate computing (AC) has become a prominent solution to improve th...
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03/03/2023
Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Machine Learning (ML) architectures have been applied to several applica...
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03/02/2023
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation
In recent years, monocular depth estimation (MDE) has witnessed a substa...
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03/02/2023
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems
Vision-based perception modules are increasingly deployed in many applic...
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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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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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03/10/2022
SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors
Specialized hardware accelerators have been designed and employed to max...
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11/13/2021
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction
Logic locking aims to prevent intellectual property (IP) piracy and unau...
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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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08/23/2021
ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories
Spiking neural networks (SNNs) have shown a potential for having low ene...
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06/16/2021
High Performance and Optimal Configuration of Accurate Heterogeneous Block-Based Approximate Adder
Approximate computing is an emerging paradigm to improve power and perfo...
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05/26/2021
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks
Continual learning is essential for all real-world applications, as froz...
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05/05/2021
Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks
From tiny pacemaker chips to aircraft collision avoidance systems, the s...
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02/28/2021
SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM
Spiking Neural Networks (SNNs) have the potential for achieving low ener...
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01/29/2021
DNN-Life: An Energy-Efficient Aging Mitigation Framework for Improving the Lifetime of On-Chip Weight Memories in Deep Neural Network Hardware Architectures
Negative Biased Temperature Instability (NBTI)-induced aging is one of t...
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12/10/2020
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking
In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less m...
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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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04/21/2020
DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks
Many convolutional neural network (CNN) accelerators face performance- a...
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12/03/2019
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
With a constant improvement in the network architectures and training me...
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12/02/2019
FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation
Deep Neural Networks (DNNs) are widely being adopted for safety-critical...
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06/11/2019
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
The state-of-the-art approaches employ approximate computing to improve ...
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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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02/22/2019
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
Approximate computing is an emerging paradigm for developing highly ener...
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02/04/2019
ROMANet: Fine-Grained Reuse-Driven Data Organization and Off-Chip Memory Access Management for Deep Neural Network Accelerators
Many hardware accelerators have been proposed to improve the computation...
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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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01/29/2019
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
Due to data dependency and model leakage properties, 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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11/05/2018
Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference
The exponential increase in dependencies between the cyber and physical ...
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11/04/2018
SSCNets: A Selective Sobel Convolution-based Technique to Enhance the Robustness of Deep Neural Networks against Security Attacks
Recent studies have shown that slight perturbations in the input data ca...
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11/04/2018
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Deep Neural Networks (DNNs) have recently been shown vulnerable to adver...
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11/02/2018
ISA4ML: Training Data-Unaware Imperceptible Security Attacks on Machine Learning Modules of Autonomous Vehicles
Due to big data analysis ability, machine learning (ML) algorithms are b...
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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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10/30/2018
MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks
The state-of-the-art accelerators for Convolutional Neural Networks (CNN...
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10/27/2018
