{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/single-path-nas-designing-hardware-efficient","title":"Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours","arxiv_id":"1904.02877","date":"2019-04-05","proceeding":null,"authors":["Dimitrios Stamoulis","Ruizhou Ding","Di Wang","Dimitrios Lymberopoulos","Bodhi Priyantha","Jie Liu","Diana Marculescu"],"abstract":"Can we automatically design a Convolutional Network (ConvNet) with the\nhighest image classification accuracy under the runtime constraint of a mobile\ndevice? Neural architecture search (NAS) has revolutionized the design of\nhardware-efficient ConvNets by automating this process. However, the NAS\nproblem remains challenging due to the combinatorially large design space,\ncausing a significant searching time (at least 200 GPU-hours). To alleviate\nthis complexity, we propose Single-Path NAS, a novel differentiable NAS method\nfor designing hardware-efficient ConvNets in less than 4 hours. Our\ncontributions are as follows: 1. Single-path search space: Compared to previous\ndifferentiable NAS methods, Single-Path NAS uses one single-path\nover-parameterized ConvNet to encode all architectural decisions with shared\nconvolutional kernel parameters, hence drastically decreasing the number of\ntrainable parameters and the search cost down to few epochs. 2.\nHardware-efficient ImageNet classification: Single-Path NAS achieves 74.96%\ntop-1 accuracy on ImageNet with 79ms latency on a Pixel 1 phone, which is\nstate-of-the-art accuracy compared to NAS methods with similar constraints\n(<80ms). 3. NAS efficiency: Single-Path NAS search cost is only 8 epochs (30\nTPU-hours), which is up to 5,000x faster compared to prior work. 4.\nReproducibility: Unlike all recent mobile-efficient NAS methods which only\nrelease pretrained models, we open-source our entire codebase at:\nhttps://github.com/dstamoulis/single-path-nas.","url_abs":"http://arxiv.org/abs/1904.02877v1","url_pdf":"http://arxiv.org/pdf/1904.02877v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/dstamoulis/single-path-nas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/osmr/imgclsmob","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/2023-MindSpore-4/Code11/tree/main/single_path_nas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/2023-MindSpore-4/Code7/tree/main/single_path_nas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/Mind23-2/MindCode-64","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/Mind23-2/MindCode-88/tree/main/single_path_nas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/code-implementation1/Code8/tree/main/single_path_nas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/cogsys-tuebingen/uninas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"single-path-nas-designing-hardware-efficient","repo_url":"https://github.com/mindspore-ai/models/tree/master/research/cv/single_path_nas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"differentiable-nas","method_name":"Differentiable NAS"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"single-path-nas","method_name":"Single-path NAS"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"Single-Path NAS","rank_in_archive_order":963,"of":1060,"metrics":{"Top 1 Accuracy":"74.96%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.02877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02877"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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