Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform
원문 초록
Event cameras are bio-inspired vision sensors that asynchronously represent pixel-level brightness changes as event streams. Event-based monocular multi-view stereo (EMVS) is a technique that exploits the event streams to estimate semi-dense 3D structure with known trajectory. It is a critical task for event-based monocular SLAM. However, the required intensive computation workloads make it challenging for real-time deployment on embedded platforms. In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA. Highly paralleled and fully pipelined processing elements are specially designed via FPGA and integrated with the embedded ARM as a heterogeneous system to improve the throughput and reduce the memory footprint. Meanwhile, the EMVS algorithm is reformulated to a more hardware-friendly manner by rescheduling, approximate computing and hybrid data quantization. Evaluation results on DAVIS dataset show that Eventor achieves up to $24\times$ improvement in energy efficiency compared with Intel i5 CPU platform.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization
Implementing Keyword Spotting on the MCXN947 Microcontroller with Integrated NPU
Variational Bayesian Channel Estimation and Data Detection for Cell-Free Massive MIMO with Low-Resolution Quantized Fronthaul Links
MNN-LLM: A Generic Inference Engine for Fast Large Language Model Deployment on Mobile Devices
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
Harnessing Large Language Models Locally: Empirical Results and Implications for AI PC
토론
이 논문과 연결된 포럼 글이 아직 없습니다.