# In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories

arXiv: https://arxiv.org/abs/2011.07095 · HTML: https://mlsanity.com/paper/2011.07095/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory](https://mlsanity.com/paper/2207.06810/) (2022)
- [MIMHD: Accurate and Efficient Hyperdimensional Inference Using Multi-Bit In-Memory Computing](https://mlsanity.com/paper/2106.12029/) (2021)
- [Associative Memory Based Experience Replay for Deep Reinforcement Learning](https://mlsanity.com/paper/2207.07791/) (2022)
- [Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network](https://mlsanity.com/paper/2103.03111/) (2021)
- [End-to-End Transformer Acceleration Through Processing-in-Memory Architectures](https://mlsanity.com/paper/2601.14260/) (2025)

## Abstract (arXiv)

Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support for accurate NN search is highly desirable. Ternary content-addressable memories (TCAMs) have been proposed to accelerate NN search for few-shot learning tasks by implementing L_∞ and Hamming distance metrics, but they cannot achieve software-comparable accuracies. This paper proposes a novel distance function that can be natively evaluated with multi-bit content-addressable memories (MCAMs) based on ferroelectric FETs (FeFETs) to perform a single-step, in-memory NN search. Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks. As an example, the proposed method achieves a 98.34% accuracy for a 5-way, 5-shot classification task for the Omniglot dataset (only 0.8% lower than software-based implementations) with a 3-bit MCAM. This represents a 13% accuracy improvement over state-of-the-art TCAM-based implementations at iso-energy and iso-delay. The presented distance function is resilient to the effects of FeFET device-to-device variations. Furthermore, this work experimentally demonstrates a 2-bit implementation of FeFET MCAM using AND arrays from GLOBALFOUNDRIES to further validate proof of concept.