Pith Number

pith:RZXN3SBP

pith:2026:RZXN3SBPEUWBCCLIV6HQA5R36O

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A3D: Agentic AI flow for autonomous Accelerator Design

Abinand Nallathambi, Anand Raghunathan, Christopher Knight, Shantanu Ganguly, Wilfried Haensch

An agentic AI flow automates the entire hardware accelerator design process for complex applications with no human intervention.

arxiv:2605.15237 v1 · 2026-05-14 · cs.AR · cs.AI

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Claims

C1strongest claim

Our implementation of A3D, using commercial components like Claude Sonnet 4.5 and the Catapult HLS tool, demonstrates its effectiveness by generating accelerator designs with no human intervention from complex scientific applications like LAMMPS (molecular dynamics simulation) and QMCPACK (quantum chemistry).

C2weakest assumption

That current LLMs, when partitioned into specialist and verifier agents and augmented with agentic RAG, can reliably perform workload analysis, identify bottlenecks, refactor code for HLS compatibility, and generate correct micro-architectures for complex irregular scientific codes without introducing errors that require human correction.

C3one line summary

A3D is an agentic AI system that automates end-to-end hardware accelerator design for complex applications like LAMMPS and QMCPACK with no human intervention.

References

37 extracted · 37 resolved · 7 Pith anchors

[1] Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al . 2024. Longbench: A bilingual, multitask benchmark for long context und 2024

[2] Fengxiang Bie, Yibo Yang, Zhongzhu Zhou, Adam Ghanem, Minjia Zhang, Zhewei Yao, Xiaoxia Wu, Connor Holmes, Pareesa Golnari, David A Clifton, et al. 2024. Renaissance: A survey into ai text-to-image ge 2024

[3] Yu-Hsin Chen, Tushar Krishna, Joel S Emer, and Vivienne Sze. 2016. Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks.IEEE journal of solid-state circuits52 2016

[4] Andrew A Chien, Allan Snavely, and Mark Gahagan. 2011. 10x10: A general- purpose architectural approach to heterogeneity and energy efficiency.Procedia Computer Science4 (2011), 1987–1996 2011

[5] Luca Collini, Siddharth Garg, and Ramesh Karri. 2025. C2hlsc: Leveraging large language models to bridge the software-to-hardware design gap.ACM Transactions on Design Automation of Electronic Systems 2025

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Receipt and verification

First computed	2026-05-20T00:00:47.813951Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8e6eddc82f252c110968af8f00763bf39c81c117748e25e6309a42ca17a645c0

Aliases

arxiv: 2605.15237 · arxiv_version: 2605.15237v1 · doi: 10.48550/arxiv.2605.15237 · pith_short_12: RZXN3SBPEUWB · pith_short_16: RZXN3SBPEUWBCCLI · pith_short_8: RZXN3SBP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RZXN3SBPEUWBCCLIV6HQA5R36O \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8e6eddc82f252c110968af8f00763bf39c81c117748e25e6309a42ca17a645c0

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c7a828d442e7a1ed174b6c43bb29d601bddf53e130c9131f03b375ccc2c8b1c1",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AR",
    "submitted_at": "2026-05-14T01:28:16Z",
    "title_canon_sha256": "26315e89bb16829fbc8148940bdb7132b537031c4e70d80ab04f9d09f379299c"
  },
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  "source": {
    "id": "2605.15237",
    "kind": "arxiv",
    "version": 1
  }
}