{"total":30,"rows":[{"id":"2503.06027","title":"Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models","created":"2025-03-08","cited_by_count":46,"area":null},{"id":"2103.00424","title":"SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments","created":"2021-02-28","cited_by_count":29,"area":null},{"id":"2107.00401","title":"CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor","created":"2021-07-01","cited_by_count":27,"area":null},{"id":"2107.00415","title":"DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks","created":"2021-07-01","cited_by_count":21,"area":null},{"id":"2108.10271","title":"ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories","created":"2021-08-23","cited_by_count":20,"area":null},{"id":"2203.05523","title":"SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors","created":"2022-03-10","cited_by_count":18,"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":"2402.11322","title":"SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems","created":"2024-02-17","cited_by_count":12,"area":null},{"id":"2201.06993","title":"Spiker: an FPGA-optimized Hardware acceleration for Spiking Neural Networks","created":"2022-01-18","cited_by_count":8,"area":null},{"id":"2410.09218","title":"Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications","created":"2024-10-11","cited_by_count":8,"area":null},{"id":"2504.00948","title":"QSViT: A Methodology for Quantizing Spiking Vision Transformers","created":"2025-04-01","cited_by_count":8,"area":null},{"id":"2404.03325","title":"Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack","created":"2024-04-04","cited_by_count":7,"area":null},{"id":"2304.04039","title":"EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems","created":"2023-04-08","cited_by_count":6,"area":null},{"id":"2304.04041","title":"RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults","created":"2023-04-08","cited_by_count":6,"area":null},{"id":"2504.06748","title":"Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation","created":"2025-04-09","cited_by_count":6,"area":null},{"id":"2404.03493","title":"A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data","created":"2024-04-04","cited_by_count":4,"area":null},{"id":"2404.09331","title":"SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents","created":"2024-04-14","cited_by_count":4,"area":null},{"id":"2504.00957","title":"Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems","created":"2025-04-01","cited_by_count":4,"area":null},{"id":"2503.17061","title":"Replay4NCL: An Efficient Memory Replay-based Methodology for Neuromorphic Continual Learning in Embedded AI Systems","created":"2025-03-21","cited_by_count":3,"area":null},{"id":"2601.00679","title":"QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models","created":"2026-01-02","cited_by_count":3,"area":null},{"id":"2404.01685","title":"SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems","created":"2024-04-02","cited_by_count":2,"area":null},{"id":"2407.05262","title":"FastSpiker: Enabling Fast Training for Spiking Neural Networks on Event-based Data through Learning Rate Enhancements for Autonomous Embedded Systems","created":"2024-07-07","cited_by_count":2,"area":null},{"id":"2407.00641","title":"NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Search","created":"2024-06-30","cited_by_count":1,"area":null},{"id":"2510.15566","title":"SpikeVox: Towards Energy-Efficient Speech Therapy Framework with Spike-driven Generative Language Models","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2606.03257","title":"PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.03428","title":"PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.04620","title":"QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.15523","title":"AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers","created":"2026-06-14","cited_by_count":0,"area":null},{"id":"2609.33207","title":"MorphAtt: A Neuromorphic Accelerator for Efficient Multi-Head Attention Processing in Spiking Vision Transformers","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.33232","title":"MTLiquid: Enabling Efficient Multi-Task Learning using Liquid Neural Networks for Lightweight Healthcare Monitoring Systems","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}