# Efficient On-Chip Implementation of 4D Radar-Based 3D Object Detection on Hailo-8L Source: [Efficient On-Chip Implementation of 4D Radar-Based 3D Object Detection on Hailo-8L]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:51:53.974Z Authors: Woong-Chan Byun, Dong-Hee Paek, Seung-Hyun Song, Seung-Hyun Kong Published: 2025-05-01T12:10:04.000Z ## Abstract 4D radar has attracted attention in autonomous driving due to its ability to enable robust 3D object detection even under adverse weather conditions. To practically deploy such technologies, it is essential to achieve real-time processing within low-power embedded environments. Addressing this, we present the first on-chip implementation of a 4D radar-based 3D object detection model on the Hailo-8L AI accelerator. Although conventional 3D convolutional neural network (CNN) architectures require 5D inputs, the Hailo-8L only supports 4D tensors, posing a significant challenge. To overcome this limitation, we introduce a tensor transformation method that reshapes 5D inputs into 4D formats during the compilation process, enabling direct deployment without altering the model structure. The proposed system achieves 46.47% AP\_3D and 52.75% AP\_BEV, maintaining comparable accuracy to GPU-based models while achieving an inference speed of 13.76 Hz. These results demonstrate the applicability of 4D radar-based perception technologies to autonomous driving systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()