The continuous shift of computational bottlenecks to the memory access and data transfer, especially for AI applications, poses the urgent needs of re-engineering the computer architecture fundamentals. Many edge computing applications, like wearable and implantable medical devices, introduce increasingly more challenges to conventional computing systems due to the strict requirements of area and power at the edge. Emerging technologies, like Resistive RAM (RRAM), have shown a promising momentum in developing neuro-inspired analogue computing paradigms capable of achieving high classification capabilities alongside high energy efficiency. In this work, we present a novel RRAM-based Analogue Content Addressable Memory (ACAM) for on-line analogue template matching applications. This ACAM-based template matching architecture aims to achieve energy-efficient classification where low energy is of utmost importance. We are showcasing a highly tuneable novel RRAM-based ACAM pixel implemented using a commercial 180nm CMOS technology and in-house RRAM technology and exhibiting low energy dissipation of approximately 0.036pJ and 0.16pJ for mismatch and match, respectively, at 66MHz with 3V voltage supply. A proof-of-concept system-level implementation based on this novel pixel design is also implemented in 180nm.

翻译：计算瓶颈持续向内存访问和数据传输转移，尤其是在人工智能应用中，这迫切需要对计算机体系结构基础进行重新设计。许多边缘计算应用，如可穿戴和植入式医疗设备，由于对边缘设备的面积和功耗有严格要求，给传统计算系统带来了日益严峻的挑战。新兴技术，如阻变存储器（RRAM），在开发神经启发式模拟计算范式方面展现出强劲势头，该范式能够同时实现高分类能力和高能效。在本工作中，我们提出了一种新型的基于RRAM的模拟内容可寻址存储器（ACAM），用于在线模拟模板匹配应用。这种基于ACAM的模板匹配架构旨在实现高能效分类，其中低能耗至关重要。我们展示了一种高度可调谐的新型基于RRAM的ACAM像素单元，该单元采用商用180nm CMOS技术和内部RRAM技术实现，在3V电源电压、66MHz工作频率下，失配和匹配时的能耗分别约为0.036pJ和0.16pJ。基于此新型像素单元设计的原理验证系统级实现同样在180nm工艺下完成。