Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training
Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）；CY Université（CY塞尔吉巴黎大学）；ENSEA（ENSEA工程师学院）；CNRS（法国国家科学研究中心）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
