Muhammad Abdullah Hanif
Orcid: 0000-0001-9841-6132
According to our database1,
Muhammad Abdullah Hanif
authored at least 101 papers
between 2016 and 2024.
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Bibliography
2024
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications.
CoRR, 2024
MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices.
CoRR, 2024
2023
Design and Analysis of High Performance Heterogeneous Block-based Approximate Adders.
ACM Trans. Embed. Comput. Syst., November, 2023
$\tt{PoisonedGNN}$: Backdoor Attack on Graph Neural Networks-Based Hardware Security Systems.
IEEE Trans. Computers, October, 2023
SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks.
Microprocess. Microsystems, February, 2023
DAEM: A Data- and Application-Aware Error Analysis Methodology for Approximate Adders.
Inf., 2023
Inf., 2023
CoRR, 2023
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches.
CoRR, 2023
A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead.
CoRR, 2023
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook.
CoRR, 2023
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications.
CoRR, 2023
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques.
CoRR, 2023
eFAT: Improving the Effectiveness of Fault-Aware Training for Mitigating Permanent Faults in DNN Hardware Accelerators.
CoRR, 2023
RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults.
CoRR, 2023
EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems.
CoRR, 2023
PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems.
CoRR, 2023
scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation.
CoRR, 2023
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation.
CoRR, 2023
Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook.
IEEE Access, 2023
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization.
Proceedings of the International Joint Conference on Neural Networks, 2023
Proceedings of the International Joint Conference on Neural Networks, 2023
Cross-Layer Approximations for System-Level Optimizations: Challenges and Opportunities.
Proceedings of the 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2023
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2023
2022
Microprocess. Microsystems, February, 2022
GNNUnlock+: A Systematic Methodology for Designing Graph Neural Networks-Based Oracle-Less Unlocking Schemes for Provably Secure Logic Locking.
IEEE Trans. Emerg. Top. Comput., 2022
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks.
J. Intell. Robotic Syst., 2022
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems.
Proceedings of the 40th IEEE VLSI Test Symposium, 2022
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks.
Proceedings of the International Joint Conference on Neural Networks, 2022
SoftSNN: low-cost fault tolerance for spiking neural network accelerators under soft errors.
Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022
2021
ROMANet: Fine-Grained Reuse-Driven Off-Chip Memory Access Management and Data Organization for Deep Neural Network Accelerators.
IEEE Trans. Very Large Scale Integr. Syst., 2021
FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators.
IEEE Trans. Very Large Scale Integr. Syst., 2021
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2021
J. Syst. Archit., 2021
High Performance and Optimal Configuration of Accurate Heterogeneous Block-Based Approximate Adder.
CoRR, 2021
Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks.
CoRR, 2021
TiQSA: Workload Minimization in Convolutional Neural Networks Using Tile Quantization and Symmetry Approximation.
IEEE Access, 2021
ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021
Emerging Computing Devices: Challenges and Opportunities for Test and Reliability<sup>*</sup>.
Proceedings of the 26th IEEE European Test Symposium, 2021
TRe-Map: Towards Reducing the Overheads of Fault-Aware Retraining of Deep Neural Networks by Merging Fault Maps.
Proceedings of the 24th Euromicro Conference on Digital System Design, 2021
DNN-Life: An Energy-Efficient Aging Mitigation Framework for Improving the Lifetime of On-Chip Weight Memories in Deep Neural Network Hardware Architectures.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021
SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM.
Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021
Proceedings of the CODES/ISSS 2021, 2021
2020
SuperSlash: A Unified Design Space Exploration and Model Compression Methodology for Design of Deep Learning Accelerators With Reduced Off-Chip Memory Access Volume.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020
ACM J. Emerg. Technol. Comput. Syst., 2020
IEEE Des. Test, 2020
APNAS: Accuracy-and-Performance-Aware Neural Architecture Search for Neural Hardware Accelerators.
IEEE Access, 2020
Proceedings of the SCOPES '20: 23rd International Workshop on Software and Compilers for Embedded Systems, 2020
Dependable Deep Learning: Towards Cost-Efficient Resilience of Deep Neural Network Accelerators against Soft Errors and Permanent Faults.
Proceedings of the 26th IEEE International Symposium on On-Line Testing and Robust System Design, 2020
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks.
Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020
ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations.
Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020
FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation.
Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020
DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks.
Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020
PEMACx: A Probabilistic Error Analysis Methodology for Adders with Cascaded Approximate Units.
Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020
2019
X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations.
CoRR, 2019
ROMANet: Fine-Grained Reuse-Driven Data Organization and Off-Chip Memory Access Management for Deep Neural Network Accelerators.
CoRR, 2019
SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?
CoRR, 2019
CoRR, 2019
CoRR, 2019
IEEE Access, 2019
Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges.
Proceedings of the 2019 IEEE Computer Society Annual Symposium on VLSI, 2019
MemGANs: Memory Management for Energy-Efficient Acceleration of Complex Computations in Hardware Architectures for Generative Adversarial Networks.
Proceedings of the 2019 IEEE/ACM International Symposium on Low Power Electronics and Design, 2019
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks.
Proceedings of the 25th IEEE International Symposium on On-Line Testing and Robust System Design, 2019
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks.
Proceedings of the 25th IEEE International Symposium on On-Line Testing and Robust System Design, 2019
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining.
Proceedings of the International Conference on Computer-Aided Design, 2019
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2019
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2019
Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities.
Proceedings of the 56th Annual Design Automation Conference 2019, 2019
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components.
Proceedings of the 56th Annual Design Automation Conference 2019, 2019
Proceedings of the 56th Annual Design Automation Conference 2019, 2019
Proceedings of the Approximate Circuits, Methodologies and CAD., 2019
Proceedings of the Approximate Circuits, Methodologies and CAD., 2019
2018
X-DNNs: Systematic Cross-Layer Approximations for Energy-Efficient Deep Neural Networks.
J. Low Power Electron., 2018
A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks.
CoRR, 2018
SSCNets: A Selective Sobel Convolution-based Technique to Enhance the Robustness of Deep Neural Networks against Security Attacks.
CoRR, 2018
ISA4ML: Training Data-Unaware Imperceptible Security Attacks on Machine Learning Modules of Autonomous Vehicles.
CoRR, 2018
MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks.
CoRR, 2018
Robustness for Smart Cyber Physical Systems and Internet-of-Things: From Adaptive Robustness Methods to Reliability and Security for Machine Learning.
Proceedings of the 2018 IEEE Computer Society Annual Symposium on VLSI, 2018
Proceedings of the 24th IEEE International Symposium on On-Line Testing And Robust System Design, 2018
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018
Security for Machine Learning-Based Systems: Attacks and Challenges During Training and Inference.
Proceedings of the 2018 International Conference on Frontiers of Information Technology, 2018
Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications, 2018
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018
DeMAS: An efficient design methodology for building approximate adders for FPGA-based systems.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018
Error resilience analysis for systematically employing approximate computing in convolutional neural networks.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018
An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the IoT era.
Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018
Area-optimized low-latency approximate multipliers for FPGA-based hardware accelerators.
Proceedings of the 55th Annual Design Automation Conference, 2018
2017
Proceedings of the 54th Annual Design Automation Conference, 2017
2016
Proceedings of the 2016 IEEE International Conference on Image Processing, 2016