Modeling and Design for Magnetoelectric Ternary Content Addressable Memory (TCAM) - Siri Narla; Piyush Kumar; Ann Franchesca Laguna; Dayane Reis; X. Sharon Sharon; Michael Niemier; Azad Naeemi (IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2022)
This article proposes a novel magnetoelectric (ME) effect-based ternary content addressable memory (TCAM). The potential array-level write and search performances of the proposed ME-TCAM are studied using experimentally calibrated compact physical models and SPICE simulations. The voltage-controlled operation of the ME devices eliminates the large joule heating present in the current-controlled magnetic devices and their low-voltage write operation makes them more energy-efficient compared to static random access memory-based TCAMs (SRAM-TCAMs). The proposed compact TCAM outperforms its SRAM counterpart with $1.35× $ and $14.4× $ improvements in search and write energy, respectively, and its nonvolatility eliminates the standby leakage. We project an error rate below $10^{-4}$ while considering various sources of variation in magnetic and CMOS devices. At the application level, using memory-augmented neural networks (MANNs), we project a $2× $ energy-delay–area-product (EDAP) improvement over an SRAM-TCAM.