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RePIM: Joint Exploitation of Activation and Weight Repetition for In-ReRAM DNN Acceleration
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Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Keywords
AI/ML System Design
Topics
Design
DescriptionEliminating redundant computations is a common approach to improve the performance of ReRAM-based DNN accelerators. While existing practical ReRAM-based accelerators eliminate part of the redundant computations by exploiting sparsity in inputs and weights or utilizing weight patterns of DNN models, they fail to identify all the redundancy, resulting in many unnecessary computations. Thus, we propose a practical design, RePIM, that is the first to jointly exploit the repetition of both inputs and weights. Our evaluation shows that RePIM is effective in eliminating unnecessary computations, achieving an average of 15.24x speedup and 96.07% energy savings over the state-of-the-art practical ReRAM-based accelerator.