Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators

S Azizi, ME Sadeghi, M Kamal, M Pedram - arxiv preprint arxiv …, 2024 - arxiv.org
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 …

Enhancing Layout Hotspot Detection Efficiency with YOLOv8 and PCA-Guided Augmentation

D Wu, S Wang, M Kamal, M Pedram - arxiv preprint arxiv:2407.14498, 2024 - arxiv.org
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 …

MENAGE: Mixed-Signal Event-Driven Neuromorphic Accelerator for Edge Applications

A Abdollahi, M Kamal, M Pedram - arxiv preprint arxiv:2410.08403, 2024 - arxiv.org
This paper presents a mixed-signal neuromorphic accelerator architecture designed for
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

SK Narayanaswami, G Srinivasan… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …