{"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/bursting-spikes-efficient-and-high","title":"Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks","arxiv_id":"2311.14265","date":"2023-11-24","proceeding":null,"authors":["Ziqing Wang","Yuetong Fang","Jiahang Cao","Renjing Xu"],"abstract":"Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Despite this, bridging the performance gap with ANNs in practical scenarios remains a significant challenge. This paper focuses on addressing the dual objectives of enhancing the performance and efficiency of SNNs through the established SNN Calibration conversion framework. Inspired by the biological nervous system, we propose a novel Adaptive-Firing Neuron Model (AdaFire) that dynamically adjusts firing patterns across different layers, substantially reducing conversion errors within limited timesteps. Moreover, to meet our efficiency objectives, we propose two novel strategies: an Sensitivity Spike Compression (SSC) technique and an Input-aware Adaptive Timesteps (IAT) technique. These techniques synergistically reduce both energy consumption and latency during the conversion process, thereby enhancing the overall efficiency of SNNs. Extensive experiments demonstrate our approach outperforms state-of-the-art SNNs methods, showcasing superior performance and efficiency in 2D, 3D, and event-driven classification, as well as object detection and segmentation tasks.","url_abs":"https://arxiv.org/abs/2311.14265v2","url_pdf":"https://arxiv.org/pdf/2311.14265v2.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":"bursting-spikes-efficient-and-high","repo_url":"https://github.com/bic-l/burst-ann2snn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"event-based-vision","task_name":"Event-based vision"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"snn","method_name":"SNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.14265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14265"}},"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. 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/bic-l/burst-ann2snn","reach":{"status":"ok"}}],"summary":{"ran":6},"by_repo_kind":{"official":{"samples":6,"ran":6,"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":6,"samples":[{"code_sha256_prefix":"418a2865512650e2","entry":"build_darknet19","repo":"bic-l/burst-ann2snn","repo_kind":"official","path":"detection/backbone/darknet19.py","file_url":"https://github.com/bic-l/burst-ann2snn/blob/HEAD/detection/backbone/darknet19.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"418a2865512650e2"}},{"code_sha256_prefix":"33fc898b0a838ed2","entry":"build_darknet53","repo":"bic-l/burst-ann2snn","repo_kind":"official","path":"detection/backbone/darknet53.py","file_url":"https://github.com/bic-l/burst-ann2snn/blob/HEAD/detection/backbone/darknet53.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"33fc898b0a838ed2"}},{"code_sha256_prefix":"38b283cfdea44e8b","entry":"compute_accuracy","repo":"bic-l/burst-ann2snn","repo_kind":"official","path":"general_utils/spiking_layer.py","file_url":"https://github.com/bic-l/burst-ann2snn/blob/HEAD/general_utils/spiking_layer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"38b283cfdea44e8b"}},{"code_sha256_prefix":"e996c64ee89d2b50","entry":"feature_transform_regularizer","repo":"bic-l/burst-ann2snn","repo_kind":"official","path":"3dtasks/models/model.py","file_url":"https://github.com/bic-l/burst-ann2snn/blob/HEAD/3dtasks/models/model.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e996c64ee89d2b50"}},{"code_sha256_prefix":"700878cc9342f3ac","entry":"find_threshold_mse","repo":"bic-l/burst-ann2snn","repo_kind":"official","path":"general_utils/spiking_layer.py","file_url":"https://github.com/bic-l/burst-ann2snn/blob/HEAD/general_utils/spiking_layer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"700878cc9342f3ac"}},{"code_sha256_prefix":"33aa9d7aced035c3","entry":"quantile","repo":"bic-l/burst-ann2snn","repo_kind":"official","path":"general_utils/spiking_layer.py","file_url":"https://github.com/bic-l/burst-ann2snn/blob/HEAD/general_utils/spiking_layer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"33aa9d7aced035c3"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}