Chimera: a block-based neural architecture search framework for event-based object detection

Abstract

Event-based cameras are sensors inspired by the human eye, offering advantages such as high-speed robustness and low power consumption. Established deep learning techniques have proven effective in processing event data, but there remains a significant space of possibilities that could be further explored to maximize the potential of such combinations. In this context, Chimera is a Block-Based Neural Architecture Search (NAS) framework specifically designed for Event-Based Object Detection, aiming to systematically adapt RGB-domain processing methods to the event domain. The Chimera design space is constructed from various macroblocks, including attention blocks, convolutions, State Space Models, and MLP-mixer-based architectures, providing a valuable trade-off between local and global processing capabilities, as well as varying levels of complexity. Results on Prophesee's GEN1 dataset demonstrated state-of-the-art mean Average Precision (mAP) while reducing the number of parameters by 1.6 × and achieving a 2.1 × speed-up. The project is available at: https://github.com/silvada95/Chimera.

More by Kamilya Smagulova

• From Algorithms to Bitstreams: A Survey of FPGA-Based Acceleration for Transformer InferencePreprints.org · 2026
• Efficient and real-time perception: a survey on end-to-end event-based object detection in autonomous drivingFrontiers in Robotics and AI · 2025 · Citations: 7
• Adversarial Robustness and Transferability on Stochastic Resistive AcceleratorsInternational Conference on Machine Intelligence and Smart Innovation · 2025
• A recurrent YOLOv8-based framework for event-based object detectionFrontiers in Neuroscience · 2025 · Citations: 19