In node classification tasks, graph convolutional neural networks (GCNs) have demonstrated competitive performance over traditional methods on diverse graph data …
Abstract Machine learning (ML) is increasingly seen as a viable approach for building compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …
Past research has proposed numerous hardware prefetching techniques, most of which rely on exploiting one specific type of program context information (eg, program counter …
Detecting semantically similar functions--a crucial analysis capability with broad real-world security usages including vulnerability detection, malware lineage, and forensics--requires …
Program execution speed critically depends on increasing cache hits, as cache hits are orders of magnitude faster than misses. To increase cache hits, we focus on the problem of …
Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Google products such as Coral and Pixel devices. In this paper, we first discuss …
Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit …
Analytical hardware performance models yield swift estimation of desired hardware performance metrics. However, developing these analytical models for modern processors …
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. This …