{"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/shallow-rnn-accurate-time-series","title":"Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Don Dennis","Durmus Alp Emre Acar","Vikram Mandikal","Vinu Sankar Sadasivan","Venkatesh Saligrama","Harsha Vardhan Simhadri","Prateek Jain"],"abstract":"Recurrent Neural Networks (RNNs) capture long dependencies and context, and\n2 hence are the key component of typical sequential data based tasks. However, the\nsequential nature of RNNs dictates a large inference cost for long sequences even if\nthe hardware supports parallelization. To induce long-term dependencies, and yet\nadmit parallelization, we introduce novel shallow RNNs. In this architecture, the\nfirst layer splits the input sequence and runs several independent RNNs. The second\nlayer consumes the output of the first layer using a second RNN thus capturing\nlong dependencies. We provide theoretical justification for our architecture under\nweak assumptions that we verify on real-world benchmarks. Furthermore, we show\nthat for time-series classification, our technique leads to substantially improved\ninference time over standard RNNs without compromising accuracy. For example,\nwe can deploy audio-keyword classification on tiny Cortex M4 devices (100MHz\nprocessor, 256KB RAM, no DSP available) which was not possible using standard\nRNN models. Similarly, using SRNN in the popular Listen-Attend-Spell (LAS)\narchitecture for phoneme classification [4], we can reduce the lag inphoneme\nclassification by 10-12x while maintaining state-of-the-art accuracy.","url_abs":"http://papers.nips.cc/paper/9451-shallow-rnn-accurate-time-series-classification-on-resource-constrained-devices","url_pdf":"http://papers.nips.cc/paper/9451-shallow-rnn-accurate-time-series-classification-on-resource-constrained-devices.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":"shallow-rnn-accurate-time-series","repo_url":"https://github.com/Microsoft/EdgeML","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"shallow-rnn-accurate-time-series","repo_url":"https://github.com/MindSpore-scientific/code-11/tree/main/shallow-rnns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"shallow-rnn-accurate-time-series","repo_url":"https://github.com/MindSpore-scientific/code-5/tree/main/shallow-rnns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"shallow-rnn-accurate-time-series","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/7/signSGD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"shallow-rnn-accurate-time-series","repo_url":"https://github.com/sevakon/shallow-rnns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}