Title: Synchronized Contrastive Pruning for Efficient Self-Supervised Learning. Abstract: Various self-supervised learning (SSL) methods have demonstrated strong capability in learning visual representations from unlabeled data. However, the current state-of-the-art (SoTA) SSL methods largely rely on heavy encoders to achieve comparable performance as the supervised learning counterpart. Despite the well-learned visual representations, the large-sized encoders impede the energy efficient computation, especially for resource-constrained edge computing. Prior works have utilized the sparsity-induced asymmetry to enhance the contrastive learning of dense models, but the generality between asymmetric sparsity and self-supervised learning has not been fully discovered. Furthermore, transferring the supervised sparse learning to SSL is also largely under-explored. To address the research gap in prior works, this paper investigates the correlation between in-training sparsity and SSL. In particular, we propose a novel sparse SSL algorithm, embracing the benefits of contrastiveness while exploiting high sparsity during SSL training. The proposed framework is evaluated comprehensively  with various granularities of sparsity, including element-wise sparsity, GPU-friendly N:M structured fine-grained sparsity, and hardware-specific structured sparsity. Extensive experiments across multiple datasets are performed, where the proposed method shows superior performance against the SoTA sparse learning algorithms with various SSL frameworks. Furthermore, the training speedup aided by the proposed method is evaluated with an actual DNN training accelerator model. 