Trio Adiono

About

Trio Adiono is a researcher specializing in hardware accelerator design, reinforcement learning (RL) systems, and embedded intelligent computing. His work sits at the intersection of machine learning algorithms and efficient hardware implementation, with a particular focus on deploying RL frameworks onto resource-constrained platforms such as FPGAs and System-on-Chip (SoC) architectures. Adiono's most significant contribution is the development of FARANE-Q — a Fast Parallel and Pipeline Q-Learning Accelerator — which has garnered over 20 citations since its 2022 introduction. This architecture addresses key challenges in real-time RL deployment by offering flexibility, configurability, and scalability without sacrificing computational speed or accuracy. Building on this foundation, his MazeCov-Q work extended RL accelerator applications to coverage-based navigation tasks, while his FPGA-based mobile robot controller demonstrated the practical translation of Q-Learning outputs into real-world motor control signals. Beyond hardware design, Adiono has explored applied machine learning in healthcare, examining stroke rehabilitation treadmill systems, and in robotics, leveraging YOLOv8 for autonomous exploration in disaster scenarios. Collectively, his portfolio reflects a commitment to bridging theoretical machine learning with tangible, deployable intelligent systems — making his research particularly relevant for engineers working at the frontier of embedded AI and autonomous robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   FARANE-Q: Fast Parallel and Pipeline Q-Learning Accelerator for Configurable Reinforcement Learning SoC

   20 citations · 2022
2. 2
   MazeCov-Q: An Efficient Maze-Based Reinforcement Learning Accelerator for Coverage

   5 citations · 2023
3. 3
   FARANE-Q: Fast Parallel and Pipeline Q-Learning Accelerator for Configurable Reinforcement Learning SoC

   4 citations · 2022
4. 4
   Control System for Mobile Robot using FPGA-Based Q-Learning Accelerator

   3 citations · 2022
5. 5
   Review on Machine Learning Applications in Assisted Treadmill for Stroke Rehabilitation

   2 citations · 2019
6. 6
   Exploration Robot Based On YOLOv8 Algorithm

   2 citations · 2024
7. 7
   FARANE-Q: Fast Parallel and Pipeline Q-Learning Accelerator for Configurable Reinforcement Learning SoC

   2 citations · 2022
8. 8
   FARANE-Q: Fast Parallel and Pipeline Q-Learning Accelerator for Configurable Reinforcement Learning SoC

   2 citations · 2023

Key Collaborators

Contact & Links