Energy-Efficient Cryogenic Ternary Content Addressable Memory using Ferroelectric SQUID

TL;DR

This paper presents a cryogenic ternary content addressable memory using ferroelectric SQUIDs, achieving ultra-low energy consumption and high efficiency for applications like quantum computing and hyperdimensional computing.

Contribution

It introduces a novel FeSQUID-based TCAM design that operates efficiently at cryogenic temperatures without peripheral circuitry, significantly reducing energy use compared to traditional SRAM-based TCAMs.

Findings

Ultra-low energy consumption of 1.36 aJ for binary search

Supports hyperdimensional computing with 89.4 fJ per vector comparison

Consumes over ten times less energy than 5nm FinFET-based TCAMs

Abstract

Ternary content addressable memories (TCAMs) are useful for certain computing tasks since they allow us to compare a search query with a whole dataset stored in the memory array. They can also unlock unique advantages for cryogenic applications like quantum computing, high-performance computing, and space exploration by improving speed and energy efficiency through parallel searching. This paper explores the design and implementation of a cryogenic ternary content addressable memory based on ferroelectric superconducting quantum interference devices (FeSQUIDs). The use of FeSQUID for designing the TCAM provides several unique advantages. First, we can get binary decisions (zero or non-zero voltage) for matching and mismatching conditions without using any peripheral circuitry. Moreover, the proposed TCAM needs ultra-low energy (1.36 aJ and 26.5 aJ average energy consumption for 1-bit binary and ternary search, respectively), thanks to the use of energy-efficient SQUIDs. Finally, we show the efficiency of FeSQUID through the brain-inspired application of Hyperdimensional Computing (HDC). Here, the FeSQUID-based TCAM implements the associative memory to support the highly parallel search needed in the inference step. We estimate an energy consumption of 89.4 fJ per vector comparison using a vector size of 10,000 bits. We also compare the FeSQUID-based TCAM array with the 5nm FinFET-based cryogenic SRAM-based TCAM array and observe that the proposed FeSQUID-based TCAM array consumes over one order of magnitude lower energy while performing the same task.