{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BLI67QJ6F33YGFRKQMVNPIJCXA","short_pith_number":"pith:BLI67QJ6","schema_version":"1.0","canonical_sha256":"0ad1efc13e2ef783162a832ad7a122b8186fd5abe99da63cd394eecc1cab170e","source":{"kind":"arxiv","id":"2407.04752","version":3},"attestation_state":"computed","paper":{"title":"SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CL","cs.NE"],"primary_cat":"cs.LG","authors_text":"Boyan Gao, David A. Clifton, Guoqi Li, Jiajun Zhang, Li Du, Shitao Xiao, Xingrun Xing, Zheng Zhang","submitted_at":"2024-07-05T08:37:17Z","abstract_excerpt":"Recent advancements in large language models (LLMs) with billions of parameters have improved performance in various applications, but their inference processes demand significant energy and computational resources. In contrast, the human brain, with approximately 86 billion neurons, is much more energy-efficient than LLMs with similar parameters. Inspired by this, we redesign 7$\\sim$70 billion parameter LLMs using bio-plausible spiking mechanisms, emulating the efficient behavior of the human brain. We propose the first spiking large language model, SpikeLLM. Coupled with the proposed model, "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.04752","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-05T08:37:17Z","cross_cats_sorted":["cs.CL","cs.NE"],"title_canon_sha256":"b507f659a64ce2d4c3e35a7416f7c5e049682385d40c1769a47764cd375ab9f0","abstract_canon_sha256":"94b6ab56c3bf9adf8c4591d53cf0045fa1308b67a5423cc7ec12db45d67f70ab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:55.752943Z","signature_b64":"Yyyd5WOXoaeP4UexFZAcOeYLPvWkXh/eCnViktHJS/T5uXzk2ETC6htE/S0x5io5OTA13/2ZbISPv5SmuhLFCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ad1efc13e2ef783162a832ad7a122b8186fd5abe99da63cd394eecc1cab170e","last_reissued_at":"2026-07-05T10:46:55.752462Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:55.752462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CL","cs.NE"],"primary_cat":"cs.LG","authors_text":"Boyan Gao, David A. Clifton, Guoqi Li, Jiajun Zhang, Li Du, Shitao Xiao, Xingrun Xing, Zheng Zhang","submitted_at":"2024-07-05T08:37:17Z","abstract_excerpt":"Recent advancements in large language models (LLMs) with billions of parameters have improved performance in various applications, but their inference processes demand significant energy and computational resources. In contrast, the human brain, with approximately 86 billion neurons, is much more energy-efficient than LLMs with similar parameters. Inspired by this, we redesign 7$\\sim$70 billion parameter LLMs using bio-plausible spiking mechanisms, emulating the efficient behavior of the human brain. We propose the first spiking large language model, SpikeLLM. Coupled with the proposed model, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.04752","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.04752/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.04752","created_at":"2026-07-05T10:46:55.752520+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.04752v3","created_at":"2026-07-05T10:46:55.752520+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.04752","created_at":"2026-07-05T10:46:55.752520+00:00"},{"alias_kind":"pith_short_12","alias_value":"BLI67QJ6F33Y","created_at":"2026-07-05T10:46:55.752520+00:00"},{"alias_kind":"pith_short_16","alias_value":"BLI67QJ6F33YGFRK","created_at":"2026-07-05T10:46:55.752520+00:00"},{"alias_kind":"pith_short_8","alias_value":"BLI67QJ6","created_at":"2026-07-05T10:46:55.752520+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.13016","citing_title":"Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12895","citing_title":"LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27807","citing_title":"SpikeVLA: Vision-Language-Action Models with Spiking Neural Networks","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2409.08290","citing_title":"Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2510.04595","citing_title":"SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11321","citing_title":"Winner-Take-All Spiking Transformer for Language Modeling","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12365","citing_title":"Adaptive Spiking Neurons for Vision and Language Modeling","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA","json":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA.json","graph_json":"https://pith.science/api/pith-number/BLI67QJ6F33YGFRKQMVNPIJCXA/graph.json","events_json":"https://pith.science/api/pith-number/BLI67QJ6F33YGFRKQMVNPIJCXA/events.json","paper":"https://pith.science/paper/BLI67QJ6"},"agent_actions":{"view_html":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA","download_json":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA.json","view_paper":"https://pith.science/paper/BLI67QJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.04752&json=true","fetch_graph":"https://pith.science/api/pith-number/BLI67QJ6F33YGFRKQMVNPIJCXA/graph.json","fetch_events":"https://pith.science/api/pith-number/BLI67QJ6F33YGFRKQMVNPIJCXA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA/action/storage_attestation","attest_author":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA/action/author_attestation","sign_citation":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA/action/citation_signature","submit_replication":"https://pith.science/pith/BLI67QJ6F33YGFRKQMVNPIJCXA/action/replication_record"}},"created_at":"2026-07-05T10:46:55.752520+00:00","updated_at":"2026-07-05T10:46:55.752520+00:00"}