This demo showcases heterogeneous compute execution of a LiDAR model running in real time on an edge device. The LiDAR processing, specifically 3D sparse convolution (spconv3d) network, runs on the Qualcomm Adreno GPU, while the Region Proposal Network (RPN) executes on the Qualcomm Hexagon NPU. This division of labor across specialized processors reduces on-device inference latency and maximizes overall efficiency. Additionally, a lightweight, learnable voxel removal layer that hierarchically prunes redundant voxels further reduces inference time without compromising detection accuracy. x000D
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"This Proposal is provided for review and evaluation purposes only. Do not redistribute to any third party without the express prior written consent of Qualcomm Technologies, Inc." x000D
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Implementation challenge that we tackle x000D
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LiDAR models often combine different types of operations: irregular, sparse computations (e.g., SpConv3D) and dense convolutional layers (e.g., CNNs). These operations have distinct hardware affinities—SpConv3D is better suited for SIMT-style GPUs, while CNNs benefit from SIMD-style NPUs. Efficient execution requires mapping each part of the model to the most appropriate compute unit. x000D
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Another challenge is the variability in voxel density across LiDAR frames. Not all voxels contribute meaningfully to object detection, many represent ground planes or distant background and can be safely discarded. However, identifying and removing these in a lightweight, learnable way is non-trivial.