{"total":21,"rows":[{"id":"2104.00298","title":"EfficientNetV2: Smaller Models and Faster Training","created":"2021-04-01","cited_by_count":1135,"area":null},{"id":"2104.10350","title":"Carbon Emissions and Large Neural Network Training","created":"2021-04-21","cited_by_count":567,"area":null},{"id":"2304.01433","title":"TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings","created":"2023-04-04","cited_by_count":202,"area":null},{"id":"2204.05149","title":"The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink","created":"2022-04-11","cited_by_count":184,"area":null},{"id":"2404.07236","title":"Lightweight Deep Learning for Resource-Constrained Environments: A Survey","created":"2024-04-08","cited_by_count":38,"area":null},{"id":"2105.12842","title":"A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators","created":"2021-05-26","cited_by_count":19,"area":null},{"id":"2111.10007","title":"FBNetV5: Neural Architecture Search for Multiple Tasks in One Run","created":"2021-11-19","cited_by_count":15,"area":null},{"id":"2302.06354","title":"Less is More: Selective Layer Finetuning with SubTuning","created":"2023-02-13","cited_by_count":14,"area":null},{"id":"2104.10645","title":"Measuring what Really Matters: Optimizing Neural Networks for TinyML","created":"2021-04-21","cited_by_count":8,"area":null},{"id":"2201.08539","title":"AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models","created":"2022-01-21","cited_by_count":7,"area":null},{"id":"2205.00841","title":"GPUNet: Searching the Deployable Convolution Neural Networks for GPUs","created":"2022-04-26","cited_by_count":7,"area":null},{"id":"2510.00379","title":"Composer: A Search Framework for Hybrid Neural Architecture Design","created":"2025-10-01","cited_by_count":7,"area":null},{"id":"2208.08562","title":"Restructurable Activation Networks","created":"2022-08-17","cited_by_count":6,"area":null},{"id":"2501.15014","title":"On Accelerating Edge AI: Optimizing Resource-Constrained Environments","created":"2025-01-25","cited_by_count":5,"area":null},{"id":"2207.07267","title":"ScaleNet: Searching for the Model to Scale","created":"2022-07-15","cited_by_count":4,"area":null},{"id":"2203.12412","title":"U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search","created":"2022-03-23","cited_by_count":2,"area":null},{"id":"2109.01932","title":"ISyNet: Convolutional Neural Networks design for AI accelerator","created":"2021-09-04","cited_by_count":1,"area":null},{"id":"2208.10498","title":"Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey","created":"2022-08-22","cited_by_count":1,"area":null},{"id":"2508.07590","title":"MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training","created":"2025-08-11","cited_by_count":1,"area":null},{"id":"2209.11785","title":"Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search","created":"2022-09-23","cited_by_count":0,"area":null},{"id":"2310.09554","title":"Neural network scoring for efficient computing","created":"2023-10-14","cited_by_count":0,"area":null}],"next_cursor":null}