To further optimize the proposed joint enhancement and transmission system for lower latency and sustained high performance, several strategies can be implemented:
Model Optimization: Fine-tuning the deep neural network architectures of Conv-TasNet and TransNet to reduce computational complexity and streamline processing can help decrease latency. This optimization can involve adjusting the network layers, activation functions, or parameters to enhance efficiency without compromising performance.
Parallel Processing: Implementing parallel processing techniques can distribute the computational load across multiple cores or processors, enabling faster execution and reduced latency. Utilizing specialized hardware accelerators like GPUs or TPUs can also expedite the processing speed.
Quantization: Employing quantization techniques to reduce the precision of network weights and activations can lead to faster inference times and lower latency. Quantization methods like fixed-point quantization or dynamic quantization can be applied to optimize the model for real-time processing.
Pruning: Utilizing network pruning methods to eliminate redundant or insignificant connections within the neural networks can help streamline the model and reduce latency. Pruning techniques such as magnitude-based pruning or iterative pruning can enhance the efficiency of the system.
Hardware Optimization: Tailoring the system to leverage hardware-specific optimizations, such as utilizing specialized instructions or memory access patterns, can enhance the overall performance and reduce latency. Customizing the implementation for specific hardware platforms can maximize efficiency.
Pipeline Optimization: Optimizing the data processing pipeline by minimizing redundant computations, reducing data movement, and enhancing memory access patterns can contribute to lower latency. Implementing efficient data loading, preprocessing, and post-processing steps can streamline the overall system performance.
By incorporating these optimization strategies, the joint enhancement and transmission system can achieve even lower latency while maintaining high performance levels, making it more suitable for real-time applications.