HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference
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Computer Science > Machine Learning
arXiv:2609.00450 (cs)
[Submitted on 31 Aug 2026]
Title:HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference
Authors:Chun-Ting Chen, Dongmin Han, Hangyeol Mun, Jake Hyun, Arnab Raha, Amit Agarwal, Mark Anders, Mohamed Abdelfattah, Jae-sun Seo
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Abstract:Block Quantization (BQ) is a promising approach for efficient deployment of large language models (LLMs), enabling low-precision computation with controlled accuracy degradation. Compared to scalar weight-only quantization (WoQ), BQ quantizes both weight and activation, offering higher hardware efficiency and end-to-end inference on a unified datapath, but its design space, spanning bit-width, block size, scaling, and numeric formats, remains underexplored.
We provide hardware/benchmark results through design space exploration (DSE). We find that increasing block size improves hardware efficiency by amortizing dequantization and accumulation costs, but degrades accuracy. This trade-off limits conventional BQ methods.
Motivated by this insight, we propose Hierarchical Block Quantization (HBQ). Unlike prior methods [1], [2], which use small blocks and conventional Power-of-Two (PoT) or integer-based scaling, HBQ uses large blocks to maximize efficiency and introduces low-overhead significand (SIG) scaling for second-level quantization. By allocating quantization levels effectively and accounting for distinct activation and weight distributions, SIG scaling compensates for large-block errors more effectively than prior PoT and INT schemes. HBQ-A (accurate) achieves W4A16-level accuracy using only W4A5 while requiring less silicon area than NVFP4. HBQ-E (efficient) further reduces hardware cost by 17% while maintaining higher accuracy than all existing BQ methods.
We implemented a 28nm ASIC accelerator applying HBQ to weights, activations, and KV cache, and integrated a novel partial-sum BQ scheme to further reduce EMA energy.
Compared to state-of-the-art WoQ, HBQ delivers $2.3\times$/$4.6\times$ higher area/energy efficiency at the same accuracy level; $1.6$--$3.3\times$ system energy reduction and $1.5$--$3.0\times$ speedup over prior BQ methods while providing best accuracy.
| Comments: | This work is accepted to the 59th IEEE/ACM International Symposium on Microarchitecture (MICRO 2026) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR) |
| Cite as: | arXiv:2609.00450 [cs.LG] |
| (or arXiv:2609.00450v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.00450
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Chun-Ting Chen Mr. [view email][v1] Mon, 31 Aug 2026 22:39:52 UTC (16,131 KB)
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