50 citations · 69 across the 6 of their papers we have counts for
6 papers
Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning
Semir Tatlidil, Yanqi Liu, Emily Sheetz +2
A major challenge in research involving artificial intelligence (AI) is the development of algorithms that can find solutions to problems that can generalize to different environme…
Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation
Yanqi Liu, Giuseppe Calderoni, R. Iris Bahar
Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based appr…
Workshops on Extreme Scale Design Automation (ESDA) Challenges and Opportunities for 2025 and Beyond
R. Iris Bahar, Alex K. Jones, Srinivas Katkoori +3
Integrated circuits and electronic systems, as well as design technologies, are evolving at a great rate -- both quantitatively and qualitatively. Major developments include new in…
GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments
Xiaotong Chen, Rui Chen, Zhiqiang Sui +4
Recent advancements have led to a proliferation of machine learning systems used to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and r…
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks
Soheil Hashemi, Nicholas Anthony, Hokchhay Tann +2
Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same…
Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off
Hokchhay Tann, Soheil Hashemi, R. Iris Bahar +1
We present a novel dynamic configuration technique for deep neural networks that permits step-wise energy-accuracy trade-offs during runtime. Our configuration technique adjusts th…