{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:K5DYDIVXVA4SP5EN6LLYC5W7RU","short_pith_number":"pith:K5DYDIVX","schema_version":"1.0","canonical_sha256":"574781a2b7a83927f48df2d78176df8d01f5b6495c0fdeb5de271e8c5c101015","source":{"kind":"arxiv","id":"2507.06376","version":1},"attestation_state":"computed","paper":{"title":"SLDB: An End-To-End Heterogeneous System-on-Chip Benchmark Suite for LLM-Aided Design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Elisavet Lydia Alvanaki, Kevin Lee, Luca P. Carloni","submitted_at":"2025-07-08T20:27:08Z","abstract_excerpt":"Over the last few years, Large Language Models (LLMs) have emerged as a valuable tool for Electronic Design Automation (EDA). State-of-the-art research in LLM-aided design has demonstrated the ability of LLMs to generate syntactically correct RTL code, showcasing encouraging prospects for integrating AI into the hardware design process. A key enabler of these advancements is the availability of high-quality benchmarks to evaluate new approaches. However, existing datasets and benchmarks fall short of system-level design, as they focus primarily on component-level information and low-complexity"},"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":"2507.06376","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-07-08T20:27:08Z","cross_cats_sorted":[],"title_canon_sha256":"d20b8671e3e399eaa3744cd60cba83ff89e34854d7e25ab6a6ae52d6de56eacd","abstract_canon_sha256":"358298338b6038b26735e341ae352215e462294b5c9502732bd5ba27006dc894"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:14.636814Z","signature_b64":"MnUNGXDitOaKf43ZXI6xQGj9Bed8Gk4VQvSpoAARap0SXEUz9qLaYCA3GON5LLcyLPNcLaYnTZlGPDBEXIRlCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"574781a2b7a83927f48df2d78176df8d01f5b6495c0fdeb5de271e8c5c101015","last_reissued_at":"2026-07-05T11:34:14.636242Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:14.636242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SLDB: An End-To-End Heterogeneous System-on-Chip Benchmark Suite for LLM-Aided Design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Elisavet Lydia Alvanaki, Kevin Lee, Luca P. Carloni","submitted_at":"2025-07-08T20:27:08Z","abstract_excerpt":"Over the last few years, Large Language Models (LLMs) have emerged as a valuable tool for Electronic Design Automation (EDA). State-of-the-art research in LLM-aided design has demonstrated the ability of LLMs to generate syntactically correct RTL code, showcasing encouraging prospects for integrating AI into the hardware design process. A key enabler of these advancements is the availability of high-quality benchmarks to evaluate new approaches. However, existing datasets and benchmarks fall short of system-level design, as they focus primarily on component-level information and low-complexity"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06376","kind":"arxiv","version":1},"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/2507.06376/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":"2507.06376","created_at":"2026-07-05T11:34:14.636301+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.06376v1","created_at":"2026-07-05T11:34:14.636301+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06376","created_at":"2026-07-05T11:34:14.636301+00:00"},{"alias_kind":"pith_short_12","alias_value":"K5DYDIVXVA4S","created_at":"2026-07-05T11:34:14.636301+00:00"},{"alias_kind":"pith_short_16","alias_value":"K5DYDIVXVA4SP5EN","created_at":"2026-07-05T11:34:14.636301+00:00"},{"alias_kind":"pith_short_8","alias_value":"K5DYDIVX","created_at":"2026-07-05T11:34:14.636301+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27350","citing_title":"CHIA: An open-source framework for principled, agentic AI-driven hardware/software co-design research","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27350","citing_title":"CHIA: An open-source framework for principled, agentic AI-driven hardware/software co-design research","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU","json":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU.json","graph_json":"https://pith.science/api/pith-number/K5DYDIVXVA4SP5EN6LLYC5W7RU/graph.json","events_json":"https://pith.science/api/pith-number/K5DYDIVXVA4SP5EN6LLYC5W7RU/events.json","paper":"https://pith.science/paper/K5DYDIVX"},"agent_actions":{"view_html":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU","download_json":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU.json","view_paper":"https://pith.science/paper/K5DYDIVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.06376&json=true","fetch_graph":"https://pith.science/api/pith-number/K5DYDIVXVA4SP5EN6LLYC5W7RU/graph.json","fetch_events":"https://pith.science/api/pith-number/K5DYDIVXVA4SP5EN6LLYC5W7RU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU/action/storage_attestation","attest_author":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU/action/author_attestation","sign_citation":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU/action/citation_signature","submit_replication":"https://pith.science/pith/K5DYDIVXVA4SP5EN6LLYC5W7RU/action/replication_record"}},"created_at":"2026-07-05T11:34:14.636301+00:00","updated_at":"2026-07-05T11:34:14.636301+00:00"}