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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 …
mitigating the effects of process-induced and aging-related variations of analog computing …
Enhancing Layout Hotspot Detection Efficiency with YOLOv8 and PCA-Guided Augmentation
In this paper, we present a YOLO-based framework for layout hotspot detection, aiming to
enhance the efficiency and performance of the design rule checking (DRC) process. Our …
enhance the efficiency and performance of the design rule checking (DRC) process. Our …
MENAGE: Mixed-Signal Event-Driven Neuromorphic Accelerator for Edge Applications
This paper presents a mixed-signal neuromorphic accelerator architecture designed for
accelerating inference with event-based neural network models. This fully CMOS …
accelerating inference with event-based neural network models. This fully CMOS …
QuAKE: Speeding up Model Inference Using Quick and Approximate Kernels for Exponential Non-Linearities
As machine learning gets deployed more and more widely, and model sizes continue to
grow, improving computational efficiency during model inference has become a key …
grow, improving computational efficiency during model inference has become a key …