{"total":30,"rows":[{"id":"2101.02559","title":"Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead","created":"2021-01-04","cited_by_count":41,"area":null},{"id":"2305.05750","title":"A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks","created":"2023-05-09","cited_by_count":23,"area":null},{"id":"2109.09829","title":"Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework","created":"2021-09-20","cited_by_count":17,"area":null},{"id":"2112.13544","title":"FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions","created":"2021-12-27","cited_by_count":12,"area":null},{"id":"2401.09509","title":"Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators","created":"2024-01-17","cited_by_count":9,"area":null},{"id":"2412.03630","title":"Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective","created":"2024-12-04","cited_by_count":8,"area":null},{"id":"2108.07019","title":"Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision","created":"2021-08-16","cited_by_count":7,"area":null},{"id":"2303.06931","title":"DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment","created":"2023-03-13","cited_by_count":7,"area":null},{"id":"2104.08323","title":"Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators","created":"2021-04-16","cited_by_count":6,"area":null},{"id":"2107.03096","title":"R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors","created":"2021-07-07","cited_by_count":6,"area":null},{"id":"2204.09514","title":"Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems","created":"2022-04-18","cited_by_count":6,"area":null},{"id":"2310.20349","title":"A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks","created":"2023-10-31","cited_by_count":6,"area":null},{"id":"2105.03251","title":"Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks","created":"2021-05-05","cited_by_count":4,"area":null},{"id":"2208.00328","title":"enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks","created":"2022-07-31","cited_by_count":4,"area":null},{"id":"2309.16733","title":"Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques","created":"2023-09-27","cited_by_count":4,"area":null},{"id":"2205.14420","title":"Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead","created":"2022-05-28","cited_by_count":3,"area":null},{"id":"2405.10658","title":"Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2511.02866","title":"LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2212.03475","title":"PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors","created":"2022-12-07","cited_by_count":2,"area":null},{"id":"2504.05122","title":"DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation","created":"2025-04-07","cited_by_count":2,"area":null},{"id":"2307.10244","title":"Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors","created":"2023-07-17","cited_by_count":1,"area":null},{"id":"2406.03229","title":"Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2406.06313","title":"ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs","created":"2024-06-10","cited_by_count":1,"area":null},{"id":"2407.04964","title":"ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters","created":"2024-07-06","cited_by_count":1,"area":null},{"id":"2408.16952","title":"Transient Fault Tolerant Semantic Segmentation for Autonomous Driving","created":"2024-08-30","cited_by_count":1,"area":null},{"id":"2503.04426","title":"FORTALESA: Fault-Tolerant Reconfigurable Systolic Array for DNN Inference","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2605.08594","title":"FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2312.13754","title":"Cross-Layer Optimization for Fault-Tolerant Deep Learning","created":"2023-12-21","cited_by_count":0,"area":null},{"id":"2404.09317","title":"Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator","created":"2024-04-14","cited_by_count":0,"area":null},{"id":"2411.19027","title":"Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations","created":"2024-11-28","cited_by_count":0,"area":null}],"next_cursor":null}