This thesis proposes a parallelized, generalized architecture for SNN inference under privacy-preserving constraints. Input images start in plaintext, then are quantized and encrypted using the BFV homomorphic encryption scheme allowing computation to proceed on protected data. The pipeline consists of linear stages coordinated by a handshake FSM: start/valid/ready/done. Since homomorphic encryption does not support non linear operations, we decrypt downstream of the linear stack to perform Activation_LIF. This step aims to restore noise budget and may re-encrypt to continue processing where necessary. Noise Budget blocks at encryption and after each operation (e.g., convolution, pooling, linear layer) provide essential telemetry for parameter selection and failure diagnosis.