Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers

TL;DR

This paper introduces a joint quantization and pruning method for CNNs to create hardware-efficient FSO receivers, maintaining performance with significant compression and enabling low-complexity optical communication systems.

Contribution

It presents a novel compression-aware learning approach that jointly optimizes quantization and pruning, including power-of-two weights for efficient computation, applied to FSO receiver CNNs.

Findings

1-bit quantization causes negligible performance loss.

2-bit quantization maintains full performance.

Compressed CNNs outperform traditional ML receivers without CSI.

Abstract

Towards fast, hardware-efficient, and low-complexity receivers, we propose a compression-aware learning approach and examine it on free-space optical (FSO) receivers for turbulence mitigation. The learning approach jointly quantize, prune, and train a convolutional neural network (CNN). In addition, we propose to have the CNN weights of power of two values so we replace the multiplication operations bit-shifting operations in every layer that has significant lower computational cost. The compression idea in the proposed approach is that the loss function is updated and both the quantization levels and the pruning limits are optimized in every epoch of training. The compressed CNN is examined for two levels of compression (1-bit and 2-bits) over different FSO systems. The numerical results show that the compression approach provides negligible decrease in performance in case of 1-bit quantization and the same performance in case of 2-bits quantization, compared to the full-precision CNNs. In general, the proposed IM/DD FSO receivers show better bit-error rate (BER) performance (without the need for channel state information (CSI)) compared to the maximum likelihood (ML) receivers that utilize imperfect CSI when the DL model is compressed whether with 1-bit or 2-bit quantization.