Extended Data Fig. 8: Toggle-rate-aware training results.
From: A lossless and fully parallel spintronic compute-in-memory macro for artificial intelligence chips
(a) Relationship between input toggle rate and accuracy for LeNet-5 INT4 model (dataset: MNIST). Increasing the regularization factor λ reduces the toggle rate while slightly impacting accuracy, demonstrating a tradeoff. (b) Energy efficiency improvement for LeNet-5. Reduction in toggle rate leads to notable energy efficiency gains, as illustrated by energy model estimations and chip-level measurements. (c) Relationship between input toggle rate and accuracy for ResNet-20 INT8 model (dataset: CIFAR-100). Similar to LeNet-5, increasing λ reduces the toggle rate with minimal accuracy degradation. (d) Energy efficiency improvement for ResNet-20 (dataset: CIFAR-100). Larger λ values result in more energy efficiency improvements.
