Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on
Mixed-Signal Accelerators
Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on
Mixed-Signal Accelerators
In this paper, we propose a framework to enhance the robustness of the neural models by mitigating the effects of process-induced and aging-related variations of analog computing components on the accuracy of the analog neural networks. We model these variations as the noise affecting the precision of the activations and …