13 citations · 15 across the 3 of their papers we have counts for
6 papers
Deep Reinforcement Learning for System-on-Chip: Myths and Realities
Tegg Taekyong Sung, Bo Ryu
Neural schedulers based on deep reinforcement learning (DRL) have shown considerable potential for solving real-world resource allocation problems, as they have demonstrated signif…
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement Learning
Tegg Taekyong Sung, Bo Ryu
Deep Reinforcement Learning (DRL) underlies in a simulated environment and optimizes objective goals. By extending the conventional interaction scheme, this paper proffers gym-ds3,…
SoCRATES: System-on-Chip Resource Adaptive Scheduling using Deep Reinforcement Learning
Tegg Taekyong Sung, Bo Ryu
Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip (SoC) applications, and has shown remarkable promi…
DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling
Tegg Taekyong Sung, Jeongsoo Ha, Jeewoo Kim +3
In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently sche…
Neural Heterogeneous Scheduler
Tegg Taekyong Sung, Valliappa Chockalingam, Alex Yahja +1
Access to parallel and distributed computation has enabled researchers and developers to improve algorithms and performance in many applications. Recent research has focused on nex…
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn +2
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domain…