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02/15/2023
Learning Performance-Improving Code Edits
The waning of Moore's Law has shifted the focus of the tech industry tow...
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10/13/2022
CUF: Continuous Upsampling Filters
Neural fields have rapidly been adopted for representing 3D signals, but...
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08/09/2022
Learning to Improve Code Efficiency
Improvements in the performance of computing systems, driven by Moore's ...
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10/20/2021
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Industry has gradually moved towards application-specific hardware accel...
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02/12/2021
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Most graph neural networks (GNN) perform poorly in graphs where neighbor...
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02/08/2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
We propose a general and scalable approximate sampling strategy for prob...
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02/02/2021
Apollo: Transferable Architecture Exploration
The looming end of Moore's Law and ascending use of deep learning drives...
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10/08/2020
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Energy-Based Models (EBMs) present a flexible and appealing way to repre...
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10/05/2020
Learned Hardware/Software Co-Design of Neural Accelerators
The use of deep learning has grown at an exponential rate, giving rise t...
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06/29/2020
An Imitation Learning Approach for Cache Replacement
Program execution speed critically depends on increasing cache hits, as ...
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06/15/2020
Neural Execution Engines: Learning to Execute Subroutines
A significant effort has been made to train neural networks that replica...
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06/17/2019
Learning Execution through Neural Code Fusion
As the performance of computer systems stagnates due to the end of Moore...
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03/06/2018
Learning Memory Access Patterns
The explosion in workload complexity and the recent slow-down in Moore's...
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09/01/2016
