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- W4309229072 abstract "With the increased size and complexity of state-of-the-art language models such as BERT, deploying them on resource-constrained devices has become challenging. Latency-aware Neural Architecture Search (NAS) is an effective solution for finding an efficient implementation of complex models that satisfy hardware limitations. However, collecting on-device accuracy and latency feedback would significantly slow down the search process, making NAS impractical. To address this, we propose a low-cost method that models both accuracy and latency of BERT-based models on the target device, NVIDIA Jetson TX2, and removes the hardware-related delays from the search loop. Using a Random Forest regressor, our predictors outperform the state-of-the-art and achieve up to 57x speedup while finding a set of near-optimal models." @default.
- W4309229072 created "2022-11-24" @default.
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- W4309229072 date "2022-10-01" @default.
- W4309229072 modified "2023-10-03" @default.
- W4309229072 title "Work-in-Progress: Utilizing latency and accuracy predictors for efficient hardware-aware NAS" @default.
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- W4309229072 doi "https://doi.org/10.1109/codes-isss55005.2022.00014" @default.
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