TY - GEN

T1 - Radar Object Detection on a Vector Processor Using Sparse Convolutional Neural Networks

AU - Köhler, Daniel

AU - Meinl, Frank

AU - Blume, Holger

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025/1/28

Y1 - 2025/1/28

N2 - Autonomous driving systems require performant and reliable perception, though they only possess limited computational resources, which places a high priority on the efficiency of the underlying algorithms. Radar sensors play an important role in this context, because they provide data in the form of sparse point clouds, which can be stored and processed in a condensed and efficient manner. However, this sparsity is often overlooked in the design of perception algorithms, such as convolutional object detection networks. In this work we investigate how sparse submanifold convolutions can be used to exploit this sparsity to drastically reduce the computational complexity of a CNN-based radar object detector. To this end, we propose an efficient implementation of submanifold convolutions on a vertical vector processor architecture called V2PRO, which is emulated on an FPGA board. Benchmarks on the public nuScenes dataset and an internal dataset show, that the sparse models provide competitive detection performance, while achieving average speedups of up to 27x over their dense counterparts on the considered vector processor. Finally, the sparse model deployed on the FPGA is integrated into a measurement vehicle with three front-facing high-resolution radars, to demonstrate real-time online radar object detection running at 15 Hz.

AB - Autonomous driving systems require performant and reliable perception, though they only possess limited computational resources, which places a high priority on the efficiency of the underlying algorithms. Radar sensors play an important role in this context, because they provide data in the form of sparse point clouds, which can be stored and processed in a condensed and efficient manner. However, this sparsity is often overlooked in the design of perception algorithms, such as convolutional object detection networks. In this work we investigate how sparse submanifold convolutions can be used to exploit this sparsity to drastically reduce the computational complexity of a CNN-based radar object detector. To this end, we propose an efficient implementation of submanifold convolutions on a vertical vector processor architecture called V2PRO, which is emulated on an FPGA board. Benchmarks on the public nuScenes dataset and an internal dataset show, that the sparse models provide competitive detection performance, while achieving average speedups of up to 27x over their dense counterparts on the considered vector processor. Finally, the sparse model deployed on the FPGA is integrated into a measurement vehicle with three front-facing high-resolution radars, to demonstrate real-time online radar object detection running at 15 Hz.

KW - FPGA

KW - Neural network accelerators

KW - Perception

KW - Radar object detection

KW - Sparse CNN

KW - Vector Processor

UR - http://www.scopus.com/inward/record.url?scp=85218469713&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-78377-7_10

DO - 10.1007/978-3-031-78377-7_10

M3 - Conference contribution

AN - SCOPUS:85218469713

SN - 9783031783760

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 138

EP - 154

BT - Embedded Computer Systems

A2 - Carro, Luigi

A2 - Regazzoni, Francesco

A2 - Pilato, Christian

PB - Springer Science and Business Media Deutschland GmbH

T2 - 24th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

Y2 - 29 June 2024 through 4 July 2024

ER -