A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge

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 180 nm CMOS technology and in-house RRAM technology and exhibiting low energy dissipation of approximately 0.036 pJ and 0.16 pJ for mismatch and match, respectively, at 66 MHz with 3.3 V voltage supply. A proof-of-concept system-level implementation based on this novel pixel design is also implemented in 180 nm.