{"total":14,"rows":[{"id":"2110.11417","title":"HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise","created":"2021-10-06","cited_by_count":65,"area":null},{"id":"2208.04425","title":"Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints","created":"2022-08-08","cited_by_count":25,"area":null},{"id":"2301.09254","title":"Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference","created":"2023-01-23","cited_by_count":20,"area":null},{"id":"2203.04737","title":"P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications","created":"2022-03-07","cited_by_count":16,"area":null},{"id":"2208.13092","title":"Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge","created":"2022-08-27","cited_by_count":12,"area":null},{"id":"2302.09108","title":"ViTA: A Vision Transformer Inference Accelerator for Edge Applications","created":"2023-02-17","cited_by_count":11,"area":null},{"id":"2104.08094","title":"Personalized Semi-Supervised Federated Learning for Human Activity Recognition","created":"2021-04-15","cited_by_count":9,"area":null},{"id":"2309.13015","title":"Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design","created":"2023-09-22","cited_by_count":5,"area":null},{"id":"2302.03523","title":"Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs","created":"2022-12-27","cited_by_count":4,"area":null},{"id":"2104.08314","title":"High Performance Convolution Using Sparsity and Patterns for Inference in Deep Convolutional Neural Networks","created":"2021-04-16","cited_by_count":3,"area":null},{"id":"2103.03543","title":"Artificial Neural Networks generated by Low Discrepancy Sequences","created":"2021-03-05","cited_by_count":1,"area":null},{"id":"2207.00068","title":"Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators","created":"2022-06-30","cited_by_count":1,"area":null},{"id":"2304.07613","title":"STen: Productive and Efficient Sparsity in PyTorch","created":"2023-04-15","cited_by_count":1,"area":null},{"id":"2207.07832","title":"Approximation Capabilities of Neural Networks using Morphological Perceptrons and Generalizations","created":"2022-07-16","cited_by_count":0,"area":null}],"next_cursor":null}