Adaptive Quantization for Deep Neural Network

In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption, which may not be affordable for mobile platforms. Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we propose an optimization framework for deep model quantization. First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy. Then, we propose an optimization process based on this measurement for finding optimal quantization bit-width for each layer. This is the first work that theoretically analyse the relationship between parameter quantization errors of individual layers and model accuracy. Our new quantization algorithm outperforms previous quantization optimization methods, and achieves 20-40% higher compression rate compared to equal bit-width quantization at the same model prediction accuracy.


Published in:
Thirty-Second Aaai Conference On Artificial Intelligence / Thirtieth Innovative Applications Of Artificial Intelligence Conference / Eighth Aaai Symposium On Educational Advances In Artificial Intelligence, 4596-4604
Presented at:
32nd AAAI Conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence Conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, LA, February 02-07, 2018
Year:
Mar 19 2018
Publisher:
Palo Alto, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
Laboratories:




 Record created 2018-03-19, last modified 2019-10-07


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