Search Results for author: Heechul Yun

Found 8 papers, 6 papers with code

Anytime-Lidar: Deadline-aware 3D Object Detection

no code implementations25 Aug 2022 Ahmet Soyyigit, Shuochao Yao, Heechul Yun

We propose a scheduling algorithm, which intelligently selects the subset of the components to make effective time and accuracy trade-off on the fly.

3D Object Detection Object +3

DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems

no code implementations23 Aug 2022 Michael Bechtel, QiTao Weng, Heechul Yun

We apply a state-of-art network architecture search (NAS) approach to find further optimized networks that can effectively control the car in real-time in an end-to-end manner.

PICO

Virtual Gang based Scheduling of Real-Time Tasks on Multicore Platforms

1 code implementation23 Dec 2019 Waqar Ali, Rodolfo Pellizzoni, Heechul Yun

Our approach is based on the notion of a virtual-gang, which is a group of parallel real-time tasks that are statically linked and scheduled together by a gang scheduler.

Operating Systems Distributed, Parallel, and Cluster Computing

Denial-of-Service Attacks on Shared Cache in Multicore: Analysis and Prevention

2 code implementations4 Mar 2019 Michael G Bechtel, Heechul Yun

In this paper we investigate the feasibility of denial-of-service (DoS) attacks on shared caches in multicore platforms.

Hardware Architecture Operating Systems

RT-Gang: Real-Time Gang Scheduling Framework for Safety-Critical Systems

1 code implementation3 Mar 2019 Waqar Ali, Heechul Yun

In this paper, we present RT-Gang: a novel real-time gang scheduling framework that enforces a one-gang-at-a-time policy.

Distributed, Parallel, and Cluster Computing

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

6 code implementations19 Dec 2017 Michael Garrett Bechtel, Elise McEllhiney, Minje Kim, Heechul Yun

We present DeepPicar, a low-cost deep neural network based autonomous car platform.

Other Computer Science Distributed, Parallel, and Cluster Computing Performance

Deterministic Memory Abstraction and Supporting Multicore System Architecture

2 code implementations17 Jul 2017 Farzad Farshchi, Prathap Kumar Valsan, Renato Mancuso, Heechul Yun

In this paper, we make a case that a fundamental problem that prevents efficient and predictable real-time computing on multicore is the lack of a proper memory abstraction to express memory criticality, which cuts across various layers of the system: the application, OS, and hardware.

Hardware Architecture Operating Systems Performance

Cannot find the paper you are looking for? You can Submit a new open access paper.