Authors: Parsa Moradi, Mohammad Ali Maddah-Ali

Published on: February 06, 2024

Impact Score: 8.07

Arxiv code: Arxiv:2402.04377

Summary

• What is new: Introduces NeRCC, a novel framework for straggler-resilient prediction serving systems, significantly outperforming existing solutions by up to 23%.
• Why this is important: Stragglers in prediction serving systems cause delays and inefficiency in executing inferences on input data using pre-trained machine-learning models.
• What the research proposes: NeRCC provides a three-layer framework that includes encoding and decoding regression and sampling for resilient, approximate computed predictions under straggler conditions.
• Results: Extensive experiments show NeRCC accurately approximates original predictions across different datasets and machine learning models, such as LeNet5, RepVGG, and Vision Transformer (ViT), with a significant performance improvement.

Technical Details

Technological frameworks used: NeRCC

Models used: LeNet5, RepVGG, Vision Transformer (ViT)

Data used: various datasets

Potential Impact

Cloud computing providers, AI service platforms, companies focusing on real-time data analytics and prediction services.

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