15 citations · 21 across the 4 of their papers we have counts for
4 papers
ExecuTorch -- A Unified PyTorch Solution to Run AI Models On-Device
Mergen Nachin, Digant Desai, Sicheng Stephen Jia +36
Local execution of AI on edge devices is important for low latency and offline operation. However, deploying models on diverse hardware remains fragmented, often requiring model co…
TorchAO: PyTorch-Native Training-to-Serving Model Optimization
Andrew Or, Apurva Jain, Daniel Vega-Myhre +10
We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO…
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware
Andrew Or, Haoyu Zhang, Michael J. Freedman
State-of-the-art deep learning systems such as TensorFlow and PyTorch tightly couple the model with the underlying hardware. This coupling requires the user to modify application l…
SLAQ: Quality-Driven Scheduling for Distributed Machine Learning
Haoyu Zhang, Logan Stafman, Andrew Or +1
Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that contin…