基于Y-Flash单元的存内学习自动机架构
In-Memory Learning Automata Architecture using Y-Flash Cell
AI总结：
针对机器学习架构的冯·诺依曼瓶颈，提出基于180nm工艺Y-Flash忆阻单元的存内学习自动机架构，将Tsetlin机自动机映射到单元电导范围，仿真验证其可扩展性与边缘学习能力更优。
摘要：
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·诺依曼瓶颈提供了颇具前景的解决方案，该技术中数据处理与存储均位于内存内部。本文提出一种新颖方法，采用以标准180 nm CMOS工艺制造的浮栅Y-Flash忆阻器件，这类器件具备模拟可调性、适中的器件间差异等优良特性，对于机器学习应用中的可靠决策至关重要。本文将新型机器学习算法Tsetlin Machine（TM，特塞特林机）用于存内处理架构，将TM的学习单元Automaton（自动机）映射到单个Y-Flash单元中，把自动机的状态范围转换为Y-Flash的电导范围。综合仿真结果表明，所提出的学习自动机硬件实现（尤其针对Tsetlin机）展现出更强的可扩展性与边缘端学习能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
