465 citations · 710 across the 17 of their papers we have counts for
17 papers
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh +5
Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correc…
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf +3
In this paper, we study the OOD generalization of neural algorithmic reasoning tasks, where the goal is to learn an algorithm (e.g., sorting, breadth-first search, and depth-first…
CUF: Continuous Upsampling Filters
Cristina Vasconcelos, Cengiz Oztireli, Mark Matthews +3
Neural fields have rapidly been adopted for representing 3D signals, but their application to more classical 2D image-processing has been relatively limited. In this paper, we cons…
Learning to Improve Code Efficiency
Binghong Chen, Daniel Tarlow, Kevin Swersky +5
Improvements in the performance of computing systems, driven by Moore's Law, have transformed society. As such hardware-driven gains slow down, it becomes even more important for s…
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi +2
Industry has gradually moved towards application-specific hardware accelerators in order to attain higher efficiency. While such a paradigm shift is already starting to show promis…
Apollo: Transferable Architecture Exploration
Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin +7
The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture explor…