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Injecting calibrated noise from RRAM's inherent randomness allows for high-utility, privacy-preserving training with minimal accuracy loss and substantial energy savings.
FusionCIM slashes LLM inference energy costs by nearly 4x while doubling processing speed, setting a new benchmark for efficiency in AI hardware.
