Seminar: Graduate Seminar

ECE Women Community

AMED: Automatic Mixed-Precision Quantization for Edge Devices

Date: February,16,2023 Start Time: 13:30 - 14:30
Location: 1061, Meyer Building
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Lecturer: Moshe Kimhi
Affiliations: The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering

Mixed-precision quantization offers better utilization of customized hardware that supports arithmetic operations at different precision.

Hardware-aware quantization methods commonly optimize a dependent variable (such as FLOPs) for a specified property of the model or induce constraints on the model size.

Both makes the model’s performance inefficient when deployed on specific hardware. Our work proposes Automatic Mixed-Precision Quantization for Edge Devices (AMED), which, during the training
procedure, quantizes the model to a different precision, looks at the bit allocation as a Markov Decision Process based on direct signal from hardware architecture.

We perform a comprehensive evaluation of the proposed method demonstrates its superiority over current state-of-the-art schemes in terms of the trade-off between neural network accuracy and hardware efficiency, on different simulated hardware architectures of edge devices.


* M.Sc. student under the supervision of Prof. Avi Mendelson.


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