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Sepehr Eghbali on Muck Rack

Sepehr Eghbali

As seen in: Amazon Science
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Articles

Factorization-aware training of transformers for natural language understanding on the edge

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By Hamidreza Saghir, Samridhi Choudhary, Sepehr Eghbali, Clement Chung
| Amazon Science
Fine-tuning transformer-based models have shown to outperform other methods for many Natural Language Understanding (NLU) tasks. Recent studies to reduce the size of transformer models have achieved reductions of > 80%, making on-device inference on powerful devices possible. However, other resource-constrained devices, like those enabling voice assistants (VAs), require much further reductions.
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Pyramid dynamic inference: Encouraging faster inference via early exit boosting

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By Jixuan Wang, Ershad Banijamali, Pegah Kharazmi, Sepehr Eghbali
| Amazon Science
Large transformer-based models have demonstrated state of the art results on several Natural Language Understanding (NLU) tasks. However, their deployment comes at the cost of increased footprint and inference latency, limiting their adoption to real-time applications, especially on resource constrained devices.
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