PiQi: Partially Quantized DNN Inference on HMPSoCs
Author:
Affiliation:
1. University of Amsterdam, Amsterdam, North holland, Netherlands
2. University of Amsterdam, Amsterdam, Netherlands
3. University Van Amsterdam, Amsterdam, North Holland, Netherlands
4. University of Amsterdam, Amsterdam, North-Holland, Netherlands
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3665314.3670841
Reference21 articles.
1. Ehsan Aghapour et al. 2022. CPU-GPU Layer-Switched Low Latency CNN Inference. In DSD.
2. Ehsan Aghapour et al. 2023. PELSI: Power-Efficient Layer-Switched Inference. In RTCSA.
3. ARM-CO-UP: ARM COoperative Utilization of Processors
4. Hyunho Ahn et al. 2023. Performance Characterization of using Quantization for DNN Inference on Edge Devices: Extended Version.
5. Claudionor N. Coelho et al. 2021. Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors. Nature Machine Intelligence (2021).
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