Ferroelectric FET-based Logic-in-Memory Encoder for Hyperdimensional Computing

TL;DR

This paper introduces a ferroelectric FET-based logic-in-memory encoder for hyperdimensional computing, significantly improving energy and area efficiency in encoding modules for complex datasets like language recognition.

Contribution

It presents a novel FeFET-based encoder implementation for HD computing, outperforming prior memory-based methods in area and energy efficiency.

Findings

Achieves 91.38% accuracy on SMS Spam dataset

Outperforms prior non-volatile memory implementations in area and energy efficiency

Demonstrates effective encoding for complex datasets like language and DNA sequencing

Abstract

Hyperdimensional (HD) computing involves encoding of baseline information into large hypervectors and repeated Boolean operations to generate the output class hypervectors which are stored in an associative memory. The classification task is then performed through similarity search operation. While prior studies have focused mostly on accelerating HD search operation using TCAMs based on emerging non-volatile memories, considering the dominant contribution of the encoder module to the energy and latency landscape specifically for complex datasets such as language recognition, DNA sequencing, etc., in this work, we propose energy- and area-efficient single FDSOI ferroelectric (Fe)FET-based logic-in-memory implementations of XOR and 3-input majority gates for N-gram HD encoders. We utilize the proposed FeFET-based encoder in a HD spam filtering accelerator and show that it outperforms the prior emerging non-volatile memory-based implementations in terms of area and energy-efficiency while exhibiting a high classification accuracy of 91.38% on the SMS Spam Collection dataset.