Presentation
ZeroTetris: A Spacial Feature Similarity-based Sparse MLP Engine for Neural Volume Rendering
DescriptionNeural Volume Rendering (NVR), a novel paradigm for the long-standing problem of photo-realistic rendering of virtual worlds, has developed explosively in the past three years. The unique and substantial computational requirements of NVR pose challenge on deploying NVR to existing dedicated accelerator for neural networks. In this work, we propose ZeroTetris, a spacial feature similarity-
based sparse multilayer perceptron (MLP) hardware accelerator for NVR. By leveraging the unique similarity-based sparsity between adjacent sampling points in NVR models, ZeroTetris efficiently bypass the computation of zero activations, thereby enhancing energy efficiency. Evaluation results affirm the effectiveness of the proposed design, showcasing ZeroTetris's superior performance in both area and power efficiency compared to other dedicated sparse matrix multiplication or MLP accelerator designs.
based sparse multilayer perceptron (MLP) hardware accelerator for NVR. By leveraging the unique similarity-based sparsity between adjacent sampling points in NVR models, ZeroTetris efficiently bypass the computation of zero activations, thereby enhancing energy efficiency. Evaluation results affirm the effectiveness of the proposed design, showcasing ZeroTetris's superior performance in both area and power efficiency compared to other dedicated sparse matrix multiplication or MLP accelerator designs.
Event Type
Research Manuscript
TimeTuesday, June 255:15pm - 5:30pm PDT
Location3004, 3rd Floor
AI
Design
AI/ML, Digital, and Analog Circuits