{"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/benchmarking-keyword-spotting-efficiency-on","title":"Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware","arxiv_id":"1812.01739","date":"2018-12-04","proceeding":null,"authors":["Peter Blouw","Xuan Choo","Eric Hunsberger","Chris Eliasmith"],"abstract":"Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning\ntoolkit, we analyze the inference speed, dynamic power consumption, and energy\ncost per inference of a two-layer neural network keyword spotter trained to\nrecognize a single phrase. We perform comparative analyses of this keyword\nspotter running on more conventional hardware devices including a CPU, a GPU,\nNvidia's Jetson TX1, and the Movidius Neural Compute Stick. Our results\nindicate that for this inference application, Loihi outperforms all of these\nalternatives on an energy cost per inference basis while maintaining equivalent\ninference accuracy. Furthermore, an analysis of tradeoffs between network size,\ninference speed, and energy cost indicates that Loihi's comparative advantage\nover other low-power computing devices improves for larger networks.","url_abs":"http://arxiv.org/abs/1812.01739v2","url_pdf":"http://arxiv.org/pdf/1812.01739v2.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":"benchmarking-keyword-spotting-efficiency-on","repo_url":"https://github.com/abr/power_benchmarks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"keyword-spotting","task_name":"Keyword Spotting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.01739","atlas_url":"https://app.syntology.ai/?focus=1812.01739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01739"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/abr/power_benchmarks","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"0c14dde5fe14c3e9","entry":"make_batches","repo":"abr/power_benchmarks","repo_kind":"official","path":"run_benchmark.py","file_url":"https://github.com/abr/power_benchmarks/blob/HEAD/run_benchmark.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0c14dde5fe14c3e9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}