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Hardware-Enabled Fuzzy Inference: Architectures, Platforms, and Emerging Trends — arXiv:2608.04031

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.04031
License (as archived): CC0-1.0
Archived: 2026-10-31T11:25:39.455Z
SHA-256 of the archived text: f2d96c0ccc8a9dcf56c6eff4a7628ab4c66a70e629bddf976db6500f56e9ecec
Stable URL of this page: https://metraipolis.com/documento/dc_38eae3779080

How to cite

Hardware-Enabled Fuzzy Inference: Architectures, Platforms, and Emerging Trends — arXiv:2608.04031 — arXiv (en), https://arxiv.org/abs/2608.04031 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_38eae3779080 (Metraipolis archive dc_38eae3779080, sha256 f2d96c0ccc8a9dcf56c6eff4a7628ab4c66a70e629bddf976db6500f56e9ecec)

BibTeX

@misc{dc38eae3779080,
  title = {Hardware-Enabled Fuzzy Inference: Architectures, Platforms, and Emerging Trends — arXiv:2608.04031},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.04031},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_38eae3779080; sha256 f2d96c0ccc8a9dcf56c6eff4a7628ab4c66a70e629bddf976db6500f56e9ecec},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.04031. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Fuzzy logic systems are widely used for intelligent decision-making under uncertainty, offering interpretability and robustness across diverse applications. However, the growing demand for real-time edge intelligence has exposed the limitations of software-based fuzzy inference: unpredictable latency, excessive power consumption, and inefficient resource utilization. This has motivated extensive research into hardware acceleration, spanning platforms from custom analog circuits and digital ASICs to reconfigurable FPGAs and ultra-low-power microcontrollers. This survey presents the first comprehensive, platform-centric review of hardware fuzzy systems, systematically organizing the literature into three principal categories: FPGA-based implementations, ASIC and custom VLSI realizations, and embedded, IoT, and TinyML platforms. For each category, we analyze architectural organization, resource mapping strategies, implementation trade-offs, and key design challenges. Our cross-platform comparative analysis reveals that no single platform dominates across all metrics. FPGAs offer flexibility and rapid prototyping, ASICs deliver peak performance and energy efficiency, while embedded and TinyML systems balance low power and cost for edge deployment. Despite significant progress, critical research gaps persist: the absence of standardized benchmarks, limited scalability of rule bases, insufficient design automation, and limited support for online learning and emerging memory technologies. We outline future directions including in-memory fuzzy computing with memristive crossbars, integration with TinyML ecosystems, explainable hardware AI, and open-source design automation. This survey serves as a {reference for} researchers and practitioners working on hardware-enabled fuzzy intelligence.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-06 in cs.AR).
Categorie arXiv: Hardware Architecture (cs.AR).
Pagina del lavoro: https://arxiv.org/abs/2608.04031 — 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 2026-10-31T11:14:40.697Z dalla pagina https://arxiv.org/catchup/cs.AR/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).