Victor Cheng

Papers

Total Citations

H-Index

About

Victor Cheng is a leading researcher in heterogeneous computing and embedded vision systems, with a focus on maximizing performance across CPU, GPU, HWA, and DSP architectures. His seminal work, "Novel OpenVX implementation for heterogeneous multi-core systems" (2017), addresses a critical challenge in modern computer vision: efficiently distributing computational workloads across diverse processing elements to achieve high utilization and low latency. This contribution has been foundational for real-time vision applications in automotive, robotics, AR/VR, and industrial machine vision, earning 5 citations that underscore its practical significance. Cheng’s research bridges the gap between hardware heterogeneity and software frameworks, enabling developers to harness the full potential of multi-core platforms without sacrificing performance. His achievements include advancing OpenVX implementations that reduce overhead and improve energy efficiency, making him a key figure in the evolution of embedded vision systems. For students and researchers, Cheng’s work offers a blueprint for tackling the complexities of heterogeneous computing, demonstrating how thoughtful system design can unlock new possibilities in autonomous systems and intelligent sensing.