no code implementations • 30 Jan 2022 • Weidong Cao, Yilong Zhao, Adith Boloor, Yinhe Han, Xuan Zhang, Li Jiang
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.
1 code implementation • 17 Oct 2020 • Jinghan Yang, Adith Boloor, Ayan Chakrabarti, Xuan Zhang, Yevgeniy Vorobeychik
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.
2 code implementations • 2 Oct 2019 • Adith Boloor, Karthik Garimella, Xin He, Christopher Gill, Yevgeniy Vorobeychik, Xuan Zhang
One such example is autonomous driving, which often relies on deep learning for perception.
no code implementations • 12 Mar 2019 • Adith Boloor, Xin He, Christopher Gill, Yevgeniy Vorobeychik, Xuan Zhang
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.