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Found 4 papers, 2 papers with code
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals
This paper presents a new PIM architecture to efficiently accelerate deep learning tasks by minimizing the required A/D conversions with analog accumulation and neural approximated peripheral circuits.
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing
We propose a scalable approach for finding adversarial modifications of a simulated autonomous driving environment using a differentiable approximation for the mapping from environmental modifications (rectangles on the road) to the corresponding video inputs to the controller neural network.
Attacking Vision-based Perception in End-to-End Autonomous Driving Models
One such example is autonomous driving, which often relies on deep learning for perception.
Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models
Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception.
