HackerPapers

arXiv:1604.00575cs.CR

AsicBoost - A Speedup for Bitcoin Mining

Timo Hanke

Abstract

AsicBoost is a method to speed up Bitcoin mining by a factor of approximately 20%. The performance gain is achieved through a high-level optimization of the Bitcoin mining algorithm which allows for drastic reduction in gate count on the mining chip. AsicBoost is applicable to all types of mining hardware and chip designs. This paper presents the idea behind the method and describes the information flow in implementations of AsicBoost.

arXiv comments
10 pages, 8 figures (2 tables, 6 drawings)
Journal reference
DOI
Subjects
cs.CRCryptography and Security

On Hacker News

Linked by 4 identifiable commenters in 5 comments across 5 threads, from Apr 2017 to Jul 2026.

Comments linking this paper, per year.

Discussed in comments5 threads

    • I'm looking forward to seeing similar work on SHA-256. It would be fascinating if AI could discover previously unknown weaknesses in reduced-round variants. It would also be interesting whether AI could discover new algorithmic optimizations for SHA-256 similar in spirit to…

      wslhView on HNParent context

    • It seems there are intersections with the history of PoW mining (CPU => GPUs => FPGAs => ASICs) since we don't need the full power of GPUs for training or inference. For example, ASICBoost [1][2][3] was/is a technique to improve PoW by ~20%. [1]…

      wslhView on HNParent context

    • I must note that this in fact happened in the past (AsicBoost). Currently, I don't think there is AsicBoost-like secret information, but you can't be sure. https://arxiv.org/abs/1604.00575

      sanxiynView on HNParent context

    • > it's basically two rounds of SHA-256. There's a bit more to this; there's clever optimisations you can do (based on the Merkle–Damgård structure of SHA256 and the format/semantics of the data being hashed): http://www.mit.edu/~jlrubin/public/pdfs/Asicboost.pdf…

      zkmsView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.