Jiashen Cao
Papers
4
Total Citations
210
H-Index
4
About
Jiashen Cao is a researcher specializing in edge computing, deep neural network (DNN) deployment, and collaborative robotics — fields that sit at the intersection of artificial intelligence and systems engineering. His work addresses one of the most pressing challenges in modern AI: bridging the gap between the computational demands of deep learning models and the constrained resources of real-world edge devices and robots. Cao's most influential contribution, "Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices" (2019), has garnered 109 citations, establishing him as a key voice in understanding how DNNs perform under the tight resource constraints of embedded hardware. His complementary work on distributed perception in collaborative robotics (88 citations) demonstrates a broader vision — enabling robot teams to collectively leverage machine learning for real-time sensor processing and complex task execution. Beyond characterization, Cao has moved toward solutions, proposing LCP, a low-communication parallelization method designed to accelerate DNN inference in resource-limited environments. Across his body of work, Cao consistently tackles the fundamental tension between model complexity and hardware feasibility, making his research highly relevant to anyone working on IoT systems, autonomous agents, or edge AI deployment.
Research Focus
Key Achievements
Top Papers
- 1Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices109 citations · 2019
- 2Distributed Perception by Collaborative Robots88 citations · 2018
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