A simulation framework for modeling and analyzing how analog noise impacts inference accuracy in SRAM-based compute-in-memory (CiM) circuits. Reveals that even 1 LSB of analog noise can significantly impair performance on complex tasks like ImageNet, and evaluates mitigation strategies — hybrid analog-digital execution and majority voting — to recover accuracy while preserving CiM energy efficiency benefits.