{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:O7YNURB6DADVKWHHUDVQ4A6C47","short_pith_number":"pith:O7YNURB6","schema_version":"1.0","canonical_sha256":"77f0da443e18075558e7a0eb0e03c2e7d3d8e8264827f7ea8b8d086b457333be","source":{"kind":"arxiv","id":"2406.00858","version":1},"attestation_state":"computed","paper":{"title":"Chiplet-Gym: Optimizing Chiplet-based AI Accelerator Design with Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Kaniz Mishty, Mehdi Sadi","submitted_at":"2024-06-02T20:30:42Z","abstract_excerpt":"Modern Artificial Intelligence (AI) workloads demand computing systems with large silicon area to sustain throughput and competitive performance. However, prohibitive manufacturing costs and yield limitations at advanced tech nodes and die-size reaching the reticle limit restrain us from achieving this. With the recent innovations in advanced packaging technologies, chiplet-based architectures have gained significant attention in the AI hardware domain. However, the vast design space of chiplet-based AI accelerator design and the absence of system and package-level co-design methodology make i"},"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":"2406.00858","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2024-06-02T20:30:42Z","cross_cats_sorted":[],"title_canon_sha256":"86b161c5e4f5c73b8332f5d216fbc6ada902ddc6bfcc698c0d8673319a72cacf","abstract_canon_sha256":"fa086fcb0787d1697b4634fc11deb7edcd227cf682c5036111b226f6f014d669"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:26:31.898328Z","signature_b64":"okjeCwQbRQ/uKJwsYIe4owcfPW+MOJ5C121JL24vBsvqrmvpawEy8XhC9GhX/F9Ucw26ViKNaSCcBibCqWPvBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77f0da443e18075558e7a0eb0e03c2e7d3d8e8264827f7ea8b8d086b457333be","last_reissued_at":"2026-07-05T08:26:31.897959Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:26:31.897959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Chiplet-Gym: Optimizing Chiplet-based AI Accelerator Design with Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Kaniz Mishty, Mehdi Sadi","submitted_at":"2024-06-02T20:30:42Z","abstract_excerpt":"Modern Artificial Intelligence (AI) workloads demand computing systems with large silicon area to sustain throughput and competitive performance. However, prohibitive manufacturing costs and yield limitations at advanced tech nodes and die-size reaching the reticle limit restrain us from achieving this. With the recent innovations in advanced packaging technologies, chiplet-based architectures have gained significant attention in the AI hardware domain. However, the vast design space of chiplet-based AI accelerator design and the absence of system and package-level co-design methodology make i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.00858","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/2406.00858/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":"2406.00858","created_at":"2026-07-05T08:26:31.898009+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.00858v1","created_at":"2026-07-05T08:26:31.898009+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00858","created_at":"2026-07-05T08:26:31.898009+00:00"},{"alias_kind":"pith_short_12","alias_value":"O7YNURB6DADV","created_at":"2026-07-05T08:26:31.898009+00:00"},{"alias_kind":"pith_short_16","alias_value":"O7YNURB6DADVKWHH","created_at":"2026-07-05T08:26:31.898009+00:00"},{"alias_kind":"pith_short_8","alias_value":"O7YNURB6","created_at":"2026-07-05T08:26:31.898009+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.00041","citing_title":"MCMComm: Hardware-Software Co-Optimization for End-to-End Communication in Multi-Chip-Modules","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47","json":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47.json","graph_json":"https://pith.science/api/pith-number/O7YNURB6DADVKWHHUDVQ4A6C47/graph.json","events_json":"https://pith.science/api/pith-number/O7YNURB6DADVKWHHUDVQ4A6C47/events.json","paper":"https://pith.science/paper/O7YNURB6"},"agent_actions":{"view_html":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47","download_json":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47.json","view_paper":"https://pith.science/paper/O7YNURB6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.00858&json=true","fetch_graph":"https://pith.science/api/pith-number/O7YNURB6DADVKWHHUDVQ4A6C47/graph.json","fetch_events":"https://pith.science/api/pith-number/O7YNURB6DADVKWHHUDVQ4A6C47/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47/action/storage_attestation","attest_author":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47/action/author_attestation","sign_citation":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47/action/citation_signature","submit_replication":"https://pith.science/pith/O7YNURB6DADVKWHHUDVQ4A6C47/action/replication_record"}},"created_at":"2026-07-05T08:26:31.898009+00:00","updated_at":"2026-07-05T08:26:31.898009+00:00"}