Husheng Zhou
Papers
2
Total Citations
93
H-Index
2
About
Husheng Zhou is a leading researcher in real-time and safety-critical embedded systems, with a primary focus on GPU-accelerated platforms for autonomous applications. His work addresses the fundamental challenge of making powerful, yet unpredictable, GPU hardware suitable for time-sensitive tasks in domains like autonomous driving and robotics. Zhou’s major contribution is the development of novel software frameworks that bridge the gap between high-performance computing and real-time guarantees. His highly cited paper, “S^3DNN: Supervised Streaming and Scheduling for GPU-Accelerated Real-Time DNN Workloads” (88 citations), pioneered a supervised scheduling approach to manage concurrent Deep Neural Network (DNN) tasks on GPUs, ensuring predictable execution without sacrificing throughput. More recently, Zhou has tackled critical security and isolation challenges in these systems. His work “gGuard: Enabling Leakage-Resilient Memory Isolation in GPU-accelerated Autonomous Embedded Systems” introduces a novel memory protection mechanism to prevent data leakage between co-located, safety-critical tasks—a vital step for certifying autonomous systems. Through his research, Zhou is not only advancing the theoretical foundations of real-time computing but also providing practical, deployable solutions for the next generation of intelligent, autonomous machines.
Research Focus
Key Achievements
Top Papers
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