88 research outputs found
SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding
Temporal Interaction Graphs (TIGs) are widely employed to model intricate
real-world systems such as financial systems and social networks. To capture
the dynamism and interdependencies of nodes, existing TIG embedding models need
to process edges sequentially and chronologically. However, this requirement
prevents it from being processed in parallel and struggle to accommodate
burgeoning data volumes to GPU. Consequently, many large-scale temporal
interaction graphs are confined to CPU processing. Furthermore, a generalized
GPU scaling and acceleration approach remains unavailable. To facilitate
large-scale TIGs' implementation on GPUs for acceleration, we introduce a novel
training approach namely Streaming Edge Partitioning and Parallel Acceleration
for Temporal Interaction Graph Embedding (SPEED). The SPEED is comprised of a
Streaming Edge Partitioning Component (SEP) which addresses space overhead
issue by assigning fewer nodes to each GPU, and a Parallel Acceleration
Component (PAC) which enables simultaneous training of different sub-graphs,
addressing time overhead issue. Our method can achieve a good balance in
computing resources, computing time, and downstream task performance. Empirical
validation across 7 real-world datasets demonstrates the potential to expedite
training speeds by a factor of up to 19.29x. Simultaneously, resource
consumption of a single-GPU can be diminished by up to 69%, thus enabling the
multiple GPU-based training and acceleration encompassing millions of nodes and
billions of edges. Furthermore, our approach also maintains its competitiveness
in downstream tasks.Comment: 13 pages, 8 figure
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar
Current perception models for different tasks usually exist in modular forms
on Unmanned Surface Vehicles (USVs), which infer extremely slowly in parallel
on edge devices, causing the asynchrony between perception results and USV
position, and leading to error decisions of autonomous navigation. Compared
with Unmanned Ground Vehicles (UGVs), the robust perception of USVs develops
relatively slowly. Moreover, most current multi-task perception models are huge
in parameters, slow in inference and not scalable. Oriented on this, we propose
Achelous, a low-cost and fast unified panoptic perception framework for
water-surface perception based on the fusion of a monocular camera and 4D
mmWave radar. Achelous can simultaneously perform five tasks, detection and
segmentation of visual targets, drivable-area segmentation, waterline
segmentation and radar point cloud segmentation. Besides, models in Achelous
family, with less than around 5 million parameters, achieve about 18 FPS on an
NVIDIA Jetson AGX Xavier, 11 FPS faster than HybridNets, and exceed YOLOX-Tiny
and Segformer-B0 on our collected dataset about 5 mAP and 0.7
mIoU, especially under situations of adverse weather, dark environments and
camera failure. To our knowledge, Achelous is the first comprehensive panoptic
perception framework combining vision-level and point-cloud-level tasks for
water-surface perception. To promote the development of the intelligent
transportation community, we release our codes in
\url{https://github.com/GuanRunwei/Achelous}.Comment: Accepted by ITSC 202
RELLIS-3D Dataset: Data, Benchmarks and Analysis
Semantic scene understanding is crucial for robust and safe autonomous
navigation, particularly so in off-road environments. Recent deep learning
advances for 3D semantic segmentation rely heavily on large sets of training
data, however existing autonomy datasets either represent urban environments or
lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal
dataset collected in an off-road environment, which contains annotations for
13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis
Campus of Texas A&M University, and presents challenges to existing algorithms
related to class imbalance and environmental topography. Additionally, we
evaluate the current state of the art deep learning semantic segmentation
models on this dataset. Experimental results show that RELLIS-3D presents
challenges for algorithms designed for segmentation in urban environments. This
novel dataset provides the resources needed by researchers to continue to
develop more advanced algorithms and investigate new research directions to
enhance autonomous navigation in off-road environments. RELLIS-3D will be
published at https://github.com/unmannedlab/RELLIS-3D
- …