In-Memory Learning Automata Architecture using Y-Flash Cell

• URL: http://arxiv.org/abs/2408.09456v1
• Date: Sun, 18 Aug 2024 12:31:54 GMT
• Title: In-Memory Learning Automata Architecture using Y-Flash Cell
• Authors: Omar Ghazal, Tian Lan, Shalman Ojukwu, Komal Krishnamurthy, Alex Yakovlev, Rishad Shafik,
• Abstract summary: In-memory computing, primarily through memristor-based analog computing, offers a promising solution to overcome this von Neumann bottleneck. Here, we introduce a novel approach that utilizes floating-gate Y-Flash memristive devices manufactured with a standard 180 nm CMOS process. This paper uses a new machine learning algorithm, the Tsetlin Machine (TM), for in-memory processing architecture.
• Score: 13.901548326102784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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