Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference

arXiv:2404.03085v1 Announce Type: cross
Abstract: On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on devices with limited resources presents a major technical challenge: practitioners need to optimize models and balance hardware metrics such as model size, latency, and power. To help practitioners create efficient ML models, we designed and developed Talaria: a model visualization and optimization system. Talaria enables practitioners to compile models to hardware, …

abstract arxiv balance challenge cloud computation cs.ai cs.hc cs.lg devices enabling hardware however inference intelligent machine machine learning machine learning models major metrics privacy resources technical type