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

TL;DR

This paper introduces a novel RRAM-based analogue content addressable memory (ACAM) for energy-efficient template matching at the edge, demonstrating low power consumption and high tunability suitable for medical and wearable devices.

Contribution

It presents a new RRAM-based ACAM architecture and pixel design, optimized for low energy consumption and implemented in 180nm CMOS technology, advancing edge AI hardware.

Findings

Achieves approximately 0.036pJ energy dissipation for mismatch detection.

Demonstrates low energy of about 0.16pJ for match detection.

Operates at 66MHz with a 3V supply in a 180nm process.

Abstract

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.