Deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have been widely applied in the many fields, such as computer vision, natural language processing, speech recognition and etc. Although DNNs achieve dramatic accuracy improvements in these real-world tasks, they require significant amounts of resources (e.g., memory, energy, storage, bandwidth and computation resources). This limits the application of these networks on resource-constrained systems, such as mobile and edge devices. A large body of literature has been proposed to addresses this problem from the perspective of compressing DNNs while preserving their performance. In this thesis, we focus on compressing deep CNNs based on vector quantization techniques.
The first part of this thesis summarizes some basic concepts in machine learning and popular techniques on model compression, including pruning, quantization, low-rank factorization and knowledge distillation approaches. Our main interest is quantization techniques, which compress networks by reducing the precision of parameters. Full-precision weights, activations and even gradients in networks can be quantized to 16-bit floating point numbers, 8-bit integers, or even binary numbers. Despite a possible performance degradation, quantization can greatly reduce the model size while maintaining model accuracy.
In the second part of this thesis, we propose a novel vector quantization approach, which we refer to as Vector Quantization with Learned Codebook, or VQLC, for CNNs. Rather than performing scalar quantization, we choose vector quantization that can simultaneously quantize multiple weights at once. Instead of taking a pretraining/clustering approach as in most works, in VQLC, the codebook for quantization are learned together with neural network training from scratch. For the forward pass, the traditional convolutional filters are replaced by the convex combinations of a set of learnable codewords. During inference, the compressed model will be represented by a small-sized codebook and a set of indices, resulting in a significant reduction of model size while preserving the network's performance.
Lastly, we validate our approach by quantizing multiple modern CNNs on several popular image classification benchmarks and compare with state-of-the-art quantization techniques. Our experimental results show that VQLC demonstrates at least comparable and often superior
performance to the existing schemes. In particular, VQLC
demonstrates significant advantages over the existing approaches
on wide networks at the high rate of compression.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/43304 |
| Date | 16 February 2022 |
| Creators | Yang, Siyuan |
| Contributors | Mao, Yongyi |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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