Design and Implementation of FPGA Based Optimized Multicore Processors

The rapid development of system-on-chip (SoC) architectures has led to increasingly complex systems facing issues such as multi-threading failures and potential failures. To address these issues, the project announced the development of a multi-student design that combines machine learning algorithms and strong cryptographic techniques to improve performance and security. Data integrity, authentication, and encryption/decryption using private and public cryptographic keys. Use advanced cryptographic algorithms such as Secure Hash Algorithm (SHA- 256) and Advanced Encryption Standard (AES) to strongly protect sensitive data. These measures ensure data integrity while also preventing unauthorized access. Exchange data on multi-core processor architecture and peripheral devices. This setting optimizes system performance, making the system suitable for real-time and mission-critical applications. Using these resources, the architecture can be efficient in machine learning while maintaining high performance. This integration allows for detection of data changes between hardware and software to ensure seamless operation and interoperability within the system. The best in security, high performance, and flexibility across the enterprise. Keywords-AHB protocol, clock register, FPGA, machine learning, support vector machine, automotive electronics design.