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Charge-CIM slashes ADC energy consumption by 91.7% while doubling throughput, reshaping the landscape of energy-efficient deep learning accelerators.
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.
Securing DNN accelerators doesn't have to break the bank: this co-design framework slashes memory overhead by 87% while boosting performance by 12%.
