16 citations · 31 across the 5 of their papers we have counts for
5 papers
On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection
Ahmet Soyyigit, Shuochao Yao, Heechul Yun
Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been shown to enhance the performance o…
TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing
Mohammed Misbah Zarrar, Qitao Weng, Bakhbyergyen Yerjan +2
Prior research has demonstrated the effectiveness of end-to-end deep learning for robotic navigation, where the control signals are directly derived from raw sensory data. However,…
VALO: A Versatile Anytime Framework for LiDAR-based Object Detection Deep Neural Networks
Ahmet Soyyigit, Shuochao Yao, Heechul Yun
This work addresses the challenge of adapting dynamic deadline requirements for LiDAR object detection deep neural networks (DNNs). The computing latency of object detection is cri…
Anytime-Lidar: Deadline-aware 3D Object Detection
Ahmet Soyyigit, Shuochao Yao, Heechul Yun
In this work, we present a novel scheduling framework enabling anytime perception for deep neural network (DNN) based 3D object detection pipelines. We focus on computationally exp…
High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators
Shamik Kundu, Ahmet Soyyiğit, Khaza Anuarul Hoque +1
The advent of data-driven real-time applications requires the implementation of Deep Neural Networks (DNNs) on Machine Learning accelerators. Google's Tensor Processing Unit (TPU)…