187 citations · 513 across the 24 of their papers we have counts for
28 papers
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Zihao Ye, Ruihang Lai, Junru Shao +2
Sparse tensors are rapidly becoming critical components of modern deep learning workloads. However, developing high-performance sparse operators can be difficult and tedious, and e…
Characterizing and Taming Resolution in Convolutional Neural Networks
Eddie Yan, Liang Luo, Luis Ceze
Image resolution has a significant effect on the accuracy and computational, storage, and bandwidth costs of computer vision model inference. These costs are exacerbated when scali…
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering
Liang Luo, Jacob Nelson, Arvind Krishnamurthy +1
ML workloads are becoming increasingly popular in the cloud. Good cloud training performance is contingent on efficient parameter exchange among VMs. We find that Collectives, the…
Pure Tensor Program Rewriting via Access Patterns (Representation Pearl)
Gus Henry Smith, Andrew Liu, Steven Lyubomirsky +5
Tensor kernels in machine learning (ML) often correspond to pure mathematical expressions, making term rewriting an attractive strategy for optimization and mapping to specialized…
Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
Chien-Yu Lin, Liang Luo, Luis Ceze
Graph neural networks (GNNs), an emerging deep learning model class, can extract meaningful representations from highly expressive graph-structured data and are therefore gaining p…
Automated Backend-Aware Post-Training Quantization
Ziheng Jiang, Animesh Jain, Andrew Liu +4
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment. However, different hardware backends such as x86 CPU, N…