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Alkaid: A Compiler Framework for Ultra-Low-Latency Kernels on Hardware — arXiv:2610.04808

Source: arXiv
Original: https://arxiv.org/abs/2610.04808
License (as archived): CC0-1.0
Archived: 2026-10-06T05:29:09.318Z
SHA-256 of the archived text: 3772cb9110b3898130bed19249dc1eecc6a8f7105c85bc973411be6892e09000
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How to cite

Alkaid: A Compiler Framework for Ultra-Low-Latency Kernels on Hardware — arXiv:2610.04808 — arXiv, https://arxiv.org/abs/2610.04808 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f3be72f8fffa (Metraipolis archive dc_f3be72f8fffa, sha256 3772cb9110b3898130bed19249dc1eecc6a8f7105c85bc973411be6892e09000)

BibTeX

@misc{dcf3be72f8fffa,
  title = {Alkaid: A Compiler Framework for Ultra-Low-Latency Kernels on Hardware — arXiv:2610.04808},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.04808},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f3be72f8fffa; sha256 3772cb9110b3898130bed19249dc1eecc6a8f7105c85bc973411be6892e09000},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.04808. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Ultra-low-latency machine learning and data processing pipelines operating on the sub-microsecond level often contain static dataflow kernels that require fine-grained bitwidth control, arithmetic optimization, and fast hardware performance estimation. This works introduce Alkaid, a free and open source domain specific compiler that translates sub-microsecond latency dataflow kernels into platform agnostic Register Transfer Level (RTL) or High-Level Synthesis (HLS)-ready code in seconds. Alkaid targets ultra-low-latency ML pipelines, especially those addressed by existing ML-to-hardware flows such as \texttt{hls4ml}, while also supporting the surrounding pre-processing, post-processing, and control logic. At its core, Alkaid uses an arithmetic centric intermediate representation, Alkaid Low-level IR (ALIR), to preserve fixed-point semantics, heterogeneous bitwidths, and operation level structure for optimization and code generation. Alkaid further provides comprehensive hardware-aware optimization passes and an interpretable white-box analytical performance model for rapid design space exploration without synthesis in the loop. Across representative ML workloads, including MLPs, GNNs, and transformers, Alkaid achieves 20-30\% lower LUT usage than the best prior flows for bit-exact equivalent designs, with up to 40\% lower latency in some cases. Beyond typical neural networks, Alkaid achieves comparable performance to the state-of-the-art method for synthesizing boosted decision trees, while also enabling the implementation and integration of non-ML kernels such as sorting networks and histograms.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.AR, nuovi lavori del giorno).
Categorie arXiv: Hardware Architecture (cs.AR) ; High Energy Physics - Experiment (hep-ex).
Pagina del lavoro: https://arxiv.org/abs/2610.04808 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:50.288Z dalla pagina https://arxiv.org/list/cs.AR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).