提出可集成新型器件的类脑芯片，支持片上学习。
TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration
- 混合信号架构支持片上学习与新型器件集成。
- 通过芯片测试验证了器件集成的可行性。
- 适合类脑计算与新型器件研究者使用。
近期在存储技术、器件和材料方面的进展为类脑电子系统集成带来了巨大潜力。然而，材料研发与大规模全功能系统实现之间仍存在显著差距。关键挑战在于确定哪些器件和材料最适合特定功能，以及如何与CMOS电路协同工作。为此，我们提出了TEXEL，一种混合信号类脑架构，旨在探索片上学习电路与新型二端和三端器件的集成。TEXEL作为一个可访问的平台，弥合了基于CMOS的类脑计算与新兴器件最新进展之间的鸿沟。本文通过全面的芯片测量和仿真，展示了TEXEL在器件集成方面的准备就绪状态。TEXEL提供了一个实用系统，可用于测试生物启发式学习算法与新兴器件的结合，建立了脑启发计算与前沿器件研究之间的切实联系。
原文摘要 · Abstract (English)
Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research.
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