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.

翻译：现代机器学习架构的实现因内存与处理单元之间频繁的数据传输而面临重大挑战。存内计算，主要通过基于忆阻器的模拟计算，为克服这一冯·诺依曼瓶颈提供了一种前景广阔的解决方案。在该技术中，数据处理与存储均位于内存内部。本文介绍了一种新颖方法，该方法利用采用标准180纳米CMOS工艺制造的浮栅Y-Flash忆阻器件。这些器件具备模拟可调性和适度的器件间差异等吸引人的特性；此类特性对于机器学习应用中可靠的决策至关重要。本文采用一种新的机器学习算法——Tsetlin Machine（TM），用于构建存内处理架构。TM的学习元件——自动机，被映射到单个Y-Flash单元中，其中自动机的状态范围被转换为Y-Flash的电导范围。通过全面仿真，所提出的学习自动机硬件实现（特别是针对Tsetlin机）已展现出增强的可扩展性和边缘学习能力。