DNN Quantization with Attention

• URL: http://arxiv.org/abs/2103.13322v1
• Date: Wed, 24 Mar 2021 16:24:59 GMT
• Title: DNN Quantization with Attention
• Authors: Ghouthi Boukli Hacene, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux
• Abstract summary: We propose a training procedure that relaxes the low-bit quantization. The relaxation is achieved by using a learnable linear combination of high, medium and low-bit quantizations. In experiments, our approach outperforms other low-bit quantization techniques.
• Score: 5.72175302235089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-bit quantization of network weights and activations can drastically reduce the memory footprint, complexity, energy consumption and latency of Deep Neural Networks (DNNs). However, low-bit quantization can also cause a considerable drop in accuracy, in particular when we apply it to complex learning tasks or lightweight DNN architectures. In this paper, we propose a training procedure that relaxes the low-bit quantization. We call this procedure \textit{DNN Quantization with Attention} (DQA). The relaxation is achieved by using a learnable linear combination of high, medium and low-bit quantizations. Our learning procedure converges step by step to a low-bit quantization using an attention mechanism with temperature scheduling. In experiments, our approach outperforms other low-bit quantization techniques on various object recognition benchmarks such as CIFAR10, CIFAR100 and ImageNet ILSVRC 2012, achieves almost the same accuracy as a full precision DNN, and considerably reduces the accuracy drop when quantizing lightweight DNN architectures.

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