# An Energy-efficient Capacitive-RRAM Content Addressable Memory

arXiv: https://arxiv.org/abs/2401.09207 · HTML: https://mlsanity.com/paper/2401.09207/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2024.

## Built on by (most cited first, 2 of 2)

- [PiC-BNN: A 128-kbit 65 nm Processing-in-CAM-Based End-to-End Binary Neural Network Accelerator](https://mlsanity.com/paper/2601.19920/) (2026)
- [An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers](https://mlsanity.com/paper/2606.14739/) (2026)

## Abstract (arXiv)

Content addressable memory is popular in intelligent computing systems as it allows parallel content-searching in memory. Emerging CAMs show a promising increase in bitcell density and a decrease in power consumption than pure CMOS solutions. This article introduced an energy-efficient 3T1R1C TCAM cooperating with capacitor dividers and RRAM devices. The RRAM as a storage element also acts as a switch to the capacitor divider while searching for content. CAM cells benefit from working parallel in an array structure. We implemented a 64 x 64 array and digital controllers to perform with an internal built-in clock frequency of 875MHz. Both data searches and reads take three clock cycles. Its worst average energy for data match is reported to be 1.71fJ/bit-search and the worst average energy for data miss is found at 4.69fJ/bit-search. The prototype is simulated and fabricated in 0.18um technology with in-lab RRAM post-processing. Such memory explores the charge domain searching mechanism and can be applied to data centers that are power-hungry.