A Benchmark for ML Inference Latency on Mobile Devices

Author:

Li Zhuojin1ORCID,Paolieri Marco1ORCID,Golubchik Leana1ORCID

Affiliation:

1. University of Southern California, Los Angeles, California, USA

Funder

NSF CNS

NSF CCF

NSF IIS

Publisher

ACM

Reference23 articles.

1. 2021. Sandbox for training deep learning networks. https://github.com/osmr/imgclsmob.

2. 2024. A Benchmark for ML Inference Latency on Mobile Devices. https://github.com/qed-usc/mobile-ml-benchmark.git.

3. Apple. 2024. Optimizing GPU performance. https://developer.apple.com/documentation/xcode/optimizing-gpu-performance.

4. Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. 2020. Once for All: Train One Network and Specialize it for Efficient Deployment. In International Conference on Learning Representations.

5. Xuanyi Dong, Lu Liu, Katarzyna Musial, and Bogdan Gabrys. 2021. Nats-bench: Benchmarking nas algorithms for architecture topology and size. IEEE transactions on pattern analysis and machine intelligence 44, 7 (2021), 3634--3646.

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