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Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference
Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home.
SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
In this paper, we propose a scalable evaluation methodology (SAIH) for analyzing the AI performance trend of HPC systems with scaling the problem sizes of customized AI applications.
EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models
Although the AI community has expanded the model scale to the trillion parameter level, the practical deployment of 10-100 billion parameter models is still uncertain due to the latency, throughput, and memory constraints.
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