The study addresses challenges in deploying deep neural networks on low-power devices. It introduces a novel quantization scheme for GRUs using Genetic Algorithms to optimize model size and accuracy. The research demonstrates improved efficiency compared to homogeneous-precision quantization across various sequential tasks.
The content discusses the importance of model compression techniques like quantization for deep neural networks, focusing on Gated Recurrent Units (GRUs). Despite advancements in compressing Convolutional Neural Networks (CNNs), quantizing RNNs remains complex due to their recurrent nature. The study explores different quantization schemes for RNNs, highlighting the challenges and potential solutions.
Neural Architecture Search (NAS) is introduced as a technique to automate neural network design by optimizing various metrics like accuracy, size, or complexity. Genetic Algorithms are specifically highlighted as an effective method within NAS for finding optimal network structures. The content emphasizes the application of GA in refining RNNs for natural language processing.
A detailed explanation of modular quantization for GRU layers is provided, outlining how each operation can be independently quantized with heterogeneous bit-widths. The process involves linear layers, element-wise operations, and activations, ensuring integer-only computation for hardware compatibility. The study also delves into the search process using Genetic Algorithms to find optimal mixed-precision quantization schemes.
Experimental evaluations across four sequential tasks demonstrate the effectiveness of the proposed approach. Results show significant reductions in model size while maintaining or improving accuracy compared to traditional homogeneous-precision quantization methods. The study concludes by discussing limitations and future directions for scaling the solution to more complex tasks.
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