Rahmat Mulyawan
Papers
4
Total Citations
28
H-Index
2
About
Rahmat Mulyawan is a researcher specializing in hardware acceleration, reinforcement learning, and System-on-Chip (SoC) design — a niche but increasingly vital intersection of machine learning and embedded systems engineering. His most prominent contribution to the field is the development of FARANE-Q (FAst paRAllel and pipeliNE Q-learning accelerator), a configurable hardware accelerator designed to implement Q-learning reinforcement learning algorithms efficiently within SoC architectures. This work, published across multiple venues in 2022 and 2023 and accumulating approximately 28 citations, addresses a critical challenge in deploying intelligent systems in resource-constrained environments: balancing computational speed, accuracy, flexibility, and scalability simultaneously. By leveraging parallel processing and pipeline architectures, Mulyawan's design enables RL algorithms to adapt to dynamic environments with increasing complexity without sacrificing performance. His research is particularly relevant for embedded AI applications, robotics, and edge computing, where efficient hardware implementations of learning algorithms are essential. For students and researchers working at the boundary of computer architecture and artificial intelligence, Mulyawan's work offers a meaningful blueprint for translating algorithmic intelligence into practical, deployable hardware systems.
Research Focus
Key Achievements
Top Papers
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