In-Memory Learning Automata Architecture using Y-Flash Cell
<|reference_start|>In-Memory Learning Automata Architecture using Y-Flash Cell: The modern implementation of machine learning architectures faces significant challenges due to frequent data transfer between memory and processing units. In-memory computing, primarily through memristor-based analog computing, offers a promising solution to overcome this von Neumann bottleneck. In this technology, data processing and storage are located inside the memory. Here, we introduce a novel approach that utilizes floating-gate Y-Flash memristive devices manufactured with a standard 180 nm CMOS process. These devices offer attractive features, including analog tunability and moderate device-to-device variation; such characteristics are essential for reliable decision-making in ML applications. This paper uses a new machine learning algorithm, the Tsetlin Machine (TM), for in-memory processing architecture. The TM's learning element, Automaton, is mapped into a single Y-Flash cell, where the Automaton's range is transferred into the Y-Flash's conductance scope. Through comprehensive simulations, the proposed hardware implementation of the learning automata, particularly for Tsetlin machines, has demonstrated enhanced scalability and on-edge learning capabilities.<|reference_end|>
@article{ghazal2024in-memory,
    title={In-Memory Learning Automata Architecture using Y-Flash Cell},
    author={Omar Ghazal, Tian Lan, Shalman Ojukwu, Komal Krishnamurthy, Alex
  Yakovlev, Rishad Shafik},
    journal={arXiv preprint arXiv:2408.09456},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09456},
    primaryClass={cs.AR cs.AI cs.ET cs.LG}
}