论文标题
绿色detnet：使用智能压缩和训练的计算和内存有效的Detnet
Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training
论文作者
论文摘要
本文介绍了一个增量培训框架，用于压缩基于流行的深度神经网络（DNN）的展开多输入 - 型 - 型号输出（MIMO）检测算法，例如Detnet。探索了增量培训的想法，以在训练时选择最佳深度。为了减少计算要求或浮点操作的数量（FLOP）和重量的稀疏性，使用组套索和稀疏组套索探索了结构化正则化的概念。与Detnet相比，我们的方法使记忆要求的$ 98.9 \％$减少了$ 81.63 \％$减少掉落的$ 98.9 \％$减少，而与Detnet相比，我们的方法减少了$ 98.9 \％$，而与Detnet相比，我们的方法令人震惊。
This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms like DetNet. The idea of incremental training is explored to select the optimal depth while training. To reduce the computation requirements or the number of FLoating point OPerations (FLOPs) and enforce sparsity in weights, the concept of structured regularization is explored using group LASSO and sparse group LASSO. Our methods lead to an astounding $98.9\%$ reduction in memory requirement and $81.63\%$ reduction in FLOPs when compared with DetNet without compromising on BER performance.
