Скачать или смотреть [ICCAD‘23] An Energy-Efficient 3D Point Cloud Neural Network Accelerator

Presented by Yuzhe Fu at ICCAD2023

Abstract—Three-dimensional (3D) point cloud has been
employed in a wide range of applications recently. As a powerful
weapon for point cloud analysis, point-based point cloud neural
networks (PNNs) have demonstrated superior performance with
less computation complexity and parameters, compared to sparse
3D convolution-based networks and graph-based convolutional
neural networks. However, point-based PNNs still suffer from
high computational redundancy, large off-chip memory access,
and low parallelism in hardware implementation, thereby
hindering the applications on edge devices. In this paper, to
address these challenges, an energy-efficient 3D point cloud
neural network accelerator is proposed for on-chip edge
computing. An efficient filter pruning scheme is used to skip the
redundant convolution of pruned filters and zero-value feature
channels. A block-wise multi-layer perceptron (MLP) fusion
method is proposed to increase the on-chip reuse of features, thereby reducing off-chip memory access. A dual-stream
blocking technique is proposed for higher parallelism while
maintaining inference accuracy. Implemented in an industrial 28-
nm CMOS technology, the proposed accelerator achieves an
effective energy efficiency of 12.65 TOPS/W and 0.13 mJ/frame
energy consumption for PointNeXt-S at 100 MHz, 0.9 V supply
voltage, and 8-bit data width. Compared to the state-of-the-art
point cloud neural network accelerators, the proposed
accelerator enhances the energy efficiency by up to 66.6× and
reduces the energy consumption per frame by up to 70.2×.