Quantization-Guided Training for Compact TinyML Models

• URL: http://arxiv.org/abs/2103.06231v1
• Date: Wed, 10 Mar 2021 18:06:05 GMT
• Title: Quantization-Guided Training for Compact TinyML Models
• Authors: Sedigh Ghamari, Koray Ozcan, Thu Dinh, Andrey Melnikov, Juan Carvajal, Jan Ernst, Sek Chai
• Abstract summary: We propose a Quantization Guided Training (QGT) method to guide DNN training towards optimized low-bit-precision targets. QGT uses customized regularization to encourage weight values towards a distribution that maximizes accuracy while reducing quantization errors.
• Score: 8.266286436571887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a Quantization Guided Training (QGT) method to guide DNN training towards optimized low-bit-precision targets and reach extreme compression levels below 8-bit precision. Unlike standard quantization-aware training (QAT) approaches, QGT uses customized regularization to encourage weight values towards a distribution that maximizes accuracy while reducing quantization errors. One of the main benefits of this approach is the ability to identify compression bottlenecks. We validate QGT using state-of-the-art model architectures on vision datasets. We also demonstrate the effectiveness of QGT with an 81KB tiny model for person detection down to 2-bit precision (representing 17.7x size reduction), while maintaining an accuracy drop of only 3% compared to a floating-point baseline.

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