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Found 7 papers, 3 papers with code
Learning Performance-Improving Code Edits
Next, we propose a broad range of adaptation strategies for code optimization; for prompting, these include retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.
Learning to Improve Code Efficiency
To automatically learn these hints from the dataset, we propose a novel discrete variational auto-encoder, where each discrete latent variable represents a different learned category of code-edit that increases performance.
An Imitation Learning Approach for Cache Replacement
While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot.
Neural Execution Engines: Learning to Execute Subroutines
A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms.
NEURAL EXECUTION ENGINES
Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.
Learning Execution through Neural Code Fusion
In this work, we propose a new approach to use GNNs to learn fused representations of general source code and its execution.
Learning Memory Access Patterns
In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance.
