Performance Evaluation of Mtj Based Ternary Content Addressable Memory Cell

Abstract

To expedite data association and location access in contemporary systems hardware search engines are replaced by software algorithms. Due to its parallel search accessibility One of the hardware search terms is content addressable memory engines. tables based on ternary CAM are deployed by network systems to classify packets. Packet classification has made it difficult to create high-performance, dependable look-up systems that are energy-efficient. However, it has significant dissipation that worsens when addressing numerous components, such as cells and related match-lines(MLs) for each search. The user provides a data word to a CAM, which then searches its entire memory to see if that data word is stored anywhere. This differs from standard computer memory, random-access memory (RAM), where the user supplies a memory address and the RAM returns the data word stored at that address.CAMs are primarily of two types: CAMs in binary (BiCAMs) Ternary CAMs (TCAMs), etc. BiCAMsCAMs that only use 1s and 0s in the stored word are the simplest kind. Additionally, TCAMs permit a third matching state of X or "don't care" for one or more of the search word's bits. Performance of content addressable Storage reliability and sensing speed affect memory.. In CAM, the sensing is carried out through match line. ML Sensing that works well reduces the ML force consumption also. Simulations should carry out up to 16 bits and should prove that the Resistive ML Sensing has less power consumption and also enhances the voltage drop between match and mismatch states than the Capacitive ML Sensing. Pre-charge Free CAM programmes are preferable alternatives to pre-charge kinds to achieve associative memory design's high-speed and low-power objectives. As a means of overcoming the energy efficient of pre-charge based designs as well as to enhance search performance, it is introduce a pre-charge PF-TCAM stands for free ternary content addressable memory. The suggested searching method increases the rate of search by cutting the time required for ML evaluation in half and removing pre-charge phase prior by conducting each search in HALF a clock cycle.