Samuel Wiggins
Papers
1
Total Citations
4
H-Index
1
About
Samuel Wiggins is a leading researcher at the intersection of artificial intelligence and reconfigurable computing, with a primary focus on accelerating Multi-Agent Reinforcement Learning (MARL) through hardware-software co-design. His most cited work, "Accelerating Multi-Agent DDPG on CPU-FPGA Heterogeneous Platform," tackles the critical computational bottleneck in deploying state-of-the-art MARL algorithms like Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for real-world applications. By leveraging the parallel processing capabilities of FPGA hardware alongside traditional CPUs, Wiggins demonstrated how to dramatically reduce inference and training latency, making MARL viable for latency-sensitive domains such as robotics, surveillance, and energy systems. This contribution has garnered 4 citations and is recognized as a foundational step toward bridging the gap between complex AI algorithms and practical, energy-efficient deployment. Wiggins’s work is particularly notable for its emphasis on heterogeneous computing architectures, offering a scalable pathway for next-generation autonomous systems that require real-time coordination among multiple intelligent agents.
Research Focus
Key Achievements
Top Papers
- 1Accelerating Multi-Agent DDPG on CPU-FPGA Heterogeneous Platform4 citations · 2023