Papers
8
Total Citations
40
H-Index
3
About
Infall Syafalni is a researcher specializing in hardware acceleration for machine learning, with a particular focus on reinforcement learning (RL) systems and embedded intelligent computing. His most recognized contribution is the development of FARANE-Q — a Fast Parallel and Pipeline Q-Learning Accelerator designed for configurable RL implementations within System-on-Chip (SoC) architectures. This work, which has accumulated over 20 citations since its 2022 publication, addresses critical challenges in dynamic environments by delivering flexibility, scalability, and computational efficiency without sacrificing accuracy. Building on this foundation, Syafalni extended his research to coverage-oriented RL with MazeCov-Q, an efficient maze-based accelerator that further demonstrates the practical applicability of hardware-accelerated learning. His work also bridges algorithm and hardware through FPGA-based mobile robot control systems driven by Q-Learning, highlighting a strong interest in real-world robotics applications. Beyond reinforcement learning, Syafalni has explored computer vision with YOLOv8-based exploration robots for disaster response and reviewed machine learning applications in stroke rehabilitation technology. Collectively, his research reflects a commitment to making intelligent algorithms faster, more practical, and deployable across embedded and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4Control System for Mobile Robot using FPGA-Based Q-Learning Accelerator3 citations · 2022
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- 6Exploration Robot Based On YOLOv8 Algorithm2 citations · 2024
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