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A Throughput-Optimized Accelerator for Submanifold Sparse Convolutional Networks
  • +2
  • Shanq-Jang Ruan,
  • Yu-Hsuan Lai,
  • Ming Fang,
  • Edwin Naroska,
  • Jeng-Lun Shieh
Shanq-Jang Ruan

Corresponding Author:[email protected]

Author Profile
Yu-Hsuan Lai
Ming Fang
Edwin Naroska
Jeng-Lun Shieh

Abstract

The 3D point cloud plays a crucial role in deep learning-based vision tasks by providing precise spatial and depth information, leading to its increasing importance in various applications. However, the sparse nature of 3D point clouds poses computational challenges. Researchers have explored the Submanifold Sparse Convolutional Network (SSCN) for processing point cloud data while preserving sparsity. Nevertheless, existing Convolutional Neural Network (CNN) accelerators encounter difficulties in effectively handling SSCNs, prompting recent studies to focus on developing dedicated accelerators for point cloud networks to improve processing performance. This brief presents a specialized hardware architecture designed for SSCNs to address the challenges of effectively processing sparse 3D point clouds. The proposed accelerator achieves a significant 2.51× improvement in throughput density compared to previous works, highlighting its effectiveness in point cloud processing.
26 Jan 2024Submitted to TechRxiv
29 Jan 2024Published in TechRxiv