Enhanced content-addressable memory with ternary contentaddressable neural network-based auto encoder

In digital electronic devices, the IC3-FPGA circuit plays a significant role in various applications in the networking, medical, and communication sectors.Fundamentally, TCAM is the chef component in the circuit responsible for the searching and pattern-matching operations.The error in the TCAM disturbs the overall functions of the IC3-FPGA circuit, such as addressing errors, data corruption, addressing errors, and much more.Besides, TCAM errors lead to hardware failure in the networking systems.Therefore, it is essential to correct the TCAM errors in order to improve the systems efficiency.To attain this, traditional researchers attempted to accomplish efficient TCAM correction but lacked efficiency.To resolve the problem, the proposed system employs ADCNN (Enhanced Content-Addressable Deep Convolutional Neural Network) to enhance the efficiency of the TCAM (Ternary Content-Addressable Memory) correction system.The CNN is utilized for the capability of translation variance, localized weight sharing, etc.; however, it lacks long-term dependencies.To resolve the issue, the presented model incorporated a genetic algorithm for the weight-based fitness value in the ADCNN.Correspondingly, the input of the respective research is the gate level inputs, where the outcome is the error-corrected TCAM.The performance of the TCAM is evaluated using performance metrics such as area, delay, and power.Moreover, a comparative analysis of the presented system with the classical model is processed to expose the efficiency of the proposed method.The respective research is intended to contribute to the research related to the improvement of the TCAM system.