Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization

• URL: http://arxiv.org/abs/2105.01868v1
• Date: Wed, 5 May 2021 05:14:22 GMT
• Title: Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization
• Authors: Byeongwook Kim, Dongsoo Lee, Yeonju Ro, Yongkweon Jeon, Se Jung Kwon, Baeseong Park, Daehwan Oh
• Abstract summary: Various post-training quant uniformization methods have usually been based on convex optimization. Our proposed technique presents higher model accuracy, especially for a low quantization.
• Score: 9.062897838978955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Various post-training uniform quantization methods have usually been studied based on convex optimization. As a result, most previous ones rely on the quantization error minimization and/or quadratic approximations. Such approaches are computationally efficient and reasonable when a large number of quantization bits are employed. When the number of quantization bits is relatively low, however, non-convex optimization is unavoidable to improve model accuracy. In this paper, we propose a new post-training uniform quantization technique considering non-convexity. We empirically show that hyper-parameters for clipping and rounding of weights and activations can be explored by monitoring task loss. Then, an optimally searched set of hyper-parameters is frozen to proceed to the next layer such that an incremental non-convex optimization is enabled for post-training quantization. Throughout extensive experimental results using various models, our proposed technique presents higher model accuracy, especially for a low-bit quantization.

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