Search Results for author: Szymon Migacz

Found 5 papers, 2 papers with code

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

1 code implementation26 Apr 2022 Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea

This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS).

Neural Architecture Search

Searching the Deployable Convolution Neural Networks for GPUs

no code implementations CVPR 2022 Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea

To achieve this goal, we build a distributed NAS system to search on a novel search space that consists of prominent factors to impact latency and accuracy.

Neural Architecture Search

Pay Attention when Required

2 code implementations9 Sep 2020 Swetha Mandava, Szymon Migacz, Alex Fit Florea

Transformer-based models consist of interleaved feed-forward blocks - that capture content meaning, and relatively more expensive self-attention blocks - that capture context meaning.

Language Modelling Paraphrase Identification +2

Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

no code implementations30 Jul 2019 Saptadeep Pal, Eiman Ebrahimi, Arslan Zulfiqar, Yaosheng Fu, Victor Zhang, Szymon Migacz, David Nellans, Puneet Gupta

This work explores hybrid parallelization, where each data parallel worker is comprised of more than one device, across which the model dataflow graph (DFG) is split using MP.

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