In‐Memory Computing with Memristor Content Addressable Memories for Pattern Matching

Abstract

The dramatic rise of data‐intensive workloads has revived application‐specific computational hardware for continuing speed and power improvements, frequently achieved by limiting data movement and implementing “in‐memory computation”. However, conventional complementary metal oxide semiconductor (CMOS) circuit designs can still suffer low power efficiency, motivating designs leveraging nonvolatile resistive random access memory (ReRAM), and with many studies focusing on crossbar circuit architectures. Another circuit primitive—content addressable memory (CAM)—shows great promise for mapping a diverse range of computational models for in‐memory computation, with recent ReRAM–CAM designs proposed but few experimentally demonstrated. Here, programming and control of memristors across an 86 × 12 memristor ternary CAM (TCAM) array integrated with CMOS are demonstrated, and parameter tradeoffs for optimizing speed and search margin are evaluated. In addition to smaller area, this memristor TCAM results in significantly lower power due to very low programmable conductance states, motivating CAM use in a wider range of computational applications than conventional TCAMs are confined to today. Finally, the first experimental demonstration of two computational models in memristor TCAM arrays is reported: regular expression matching in a finite state machine for network security intrusion detection and definable inexact pattern matching in a Levenshtein automata for genomic sequencing.

Electrolyte-Dependent Modification of Resistive Switching in Anodic Hafnia

“…Memristive effects in various materials are continuously reported and the interest for memristive devices grows due to their wide range of applications. Redox-based random access memories (ReRAMs), as a new generation of memories more progressive than FLASH technology, and dynamic random access memories (DRAM) are some of the most common applications [ 1 , 2 , 3 ]. Additionally, memristors are used as building blocks for neuromorphic architectures [ 4 ] such as artificial synapses [ 5 ] and logic circuits due to the possibility of multilevel switching to implement a fuzzy behavior [ 6 , 7 , 8 ].…”

Recent Advances on Multivalued Logic Gates: A Materials Perspective

“…This ZnO‐QD‐based device is implemented into a resistive‐load logic gate and the resultant device successfully demonstrates the feasibility of multilevel circuit operations. [ 30 ]…”

“…Thus, researchers are seeking alternatives to the conventional von Neumann architecture, in which computations can be performed using computing units that are not physically separate from memory units; such technology is termed in-memory computing. [22][23][24][25][26][27][28][29][30][31] However, technologies that are more tangible and plausible and capable of surpassing the computing performance of the conventional von Neumann architecture are also available; these technologies can serve as a platform for increasing the information density per given unit device using ternary, quaternary, or even higher multivalued logic (MVL) systems. [15][16][32][33][34] Current processing systems are based on the binary system, wherein information is stored as a "0" bit or a "1" bit; in contrast, the MVL system functions as a ternary, quaternary, or even higher-valued system, which enables significant reductions in the number of devices and the overall system complexity.…”

Memristors Based on (Zr, Hf, Nb, Ta, Mo, W) High‐Entropy Oxides

“…Memristors are non-volatile memory devices that offer advantages in high device density, ultra-low power, ease of fabrication, and large analog capacity, [1][2][3] as well as desirable functionalities for analog circuit elements, [4,5] and computing applications, including neuromorphic computing, [6][7][8][9][10][11] and in-memory computing. [12][13][14] Particularly, oxide-based memristors, or valence charge memory, whose resistivity states are determined by the oxygen vacancy distribution in a switching…”