@inproceedings{10.1145_3716368.3735157,
    author = {Lanius, Christian and Freye, Florian and Gemmeke, Tobias},
    title = {A 1.27 fJ/B/transition Digital Compute-in-Memory Architecture for Non-Deterministic Finite Automata Evaluation},
    year = {2025},
    isbn = {9798400714962},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3716368.3735157},
    doi = {10.1145/3716368.3735157},
    abstract = {Pattern matching using non-deterministic finite automata (NFA) is critical in applications such as network intrusion detection systems (IDS), but efficient acceleration of NFA computations remains a challenge. In this work, we present a high-performance accelerator for NFA, fabricated in 22 nm Fully-Depleted Silicon-on-Insulator (FD-SOI) technology. The accelerator achieves a throughput of 2822 MBs− 1 at maximum frequency and 406 MBs− 1 (1.27 fJ/B/transition) throughput (energy per operation) at the minimum energy point. We attain this performance by employing digital compute-in-memory (CIM) macros and integrating a CIM Bloom filter to gate the activity of the macros, enabling opportunistic symbol skipping. Our accelerator demonstrates significant improvements in throughput and energy efficiency, making it suitable for high-performance pattern matching applications.},
    booktitle = {Proceedings of the Great Lakes Symposium on VLSI 2025},
    pages = {185–191},
    numpages = {7},
    keywords = {compute-in-memory, non-deterministic finite automata, digital circuit design, 22nm fdSOI},
    location = {},
    series = {GLSVLSI '25},
    date-added = {2025-7-4 16:7:6 +0100}
}

@inproceedings{10.1145_3716368.3735157, author = {Lanius, Christian and Freye, Florian and Gemmeke, Tobias}, title = {A 1.27 fJ/B/transition Digital Compute-in-Memory Architecture for Non-Deterministic Finite Automata Evaluation}, year = {2025}, isbn = {9798400714962}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3716368.3735157}, doi = {10.1145/3716368.3735157}, abstract = {Pattern matching using non-deterministic finite automata (NFA) is critical in applications such as network intrusion detection systems (IDS), but efficient acceleration of NFA computations remains a challenge. In this work, we present a high-performance accelerator for NFA, fabricated in 22 nm Fully-Depleted Silicon-on-Insulator (FD-SOI) technology. The accelerator achieves a throughput of 2822 MBs− 1 at maximum frequency and 406 MBs− 1 (1.27 fJ/B/transition) throughput (energy per operation) at the minimum energy point. We attain this performance by employing digital compute-in-memory (CIM) macros and integrating a CIM Bloom filter to gate the activity of the macros, enabling opportunistic symbol skipping. Our accelerator demonstrates significant improvements in throughput and energy efficiency, making it suitable for high-performance pattern matching applications.}, booktitle = {Proceedings of the Great Lakes Symposium on VLSI 2025}, pages = {185–191}, numpages = {7}, keywords = {compute-in-memory, non-deterministic finite automata, digital circuit design, 22nm fdSOI}, location = {}, series = {GLSVLSI '25}, date-added = {2025-7-4 16:7:6 +0100} }