Flexible Quantization for Efficient Convolutional Neural Networks
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Published:2024-05-14
Issue:10
Volume:13
Page:1923
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Zacchigna Federico Giordano1ORCID, Lew Sergio23ORCID, Lutenberg Ariel13ORCID
Affiliation:
1. Universidad de Buenos Aires, Facultad de Ingeniería (FIUBA), Laboratorio de Sistemas Embebidos (LSE), Buenos Aires C1063ACV, Argentina 2. Universidad de Buenos Aires, Facultad de Ingeniería (FIUBA), Instituto de Ingeniería Biomédica (IBYME), Buenos Aires C1063ACV, Argentina 3. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425FQB, Argentina
Abstract
This work focuses on the efficient quantization of convolutional neural networks (CNNs). Specifically, we introduce a method called non-uniform uniform quantization (NUUQ), a novel quantization methodology that combines the benefits of non-uniform quantization, such as high compression levels, with the advantages of uniform quantization, which enables an efficient implementation in fixed-point hardware. NUUQ is based on decoupling the quantization levels from the number of bits. This decoupling allows for a trade-off between the spatial and temporal complexity of the implementation, which can be leveraged to further reduce the spatial complexity of the CNN, without a significant performance loss. Additionally, we explore different quantization configurations and address typical use cases. The NUUQ algorithm demonstrates the capability to achieve compression levels equivalent to 2 bits without an accuracy loss and even levels equivalent to ∼1.58 bits, but with a loss in performance of only ∼0.6%.
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