Vladimir Sidorenko
Papers
1
Total Citations
11
H-Index
1
About
Vladimir Sidorenko is a researcher at the forefront of efficient deep learning deployment, specializing in the optimization of Convolutional Neural Networks (CNNs) for resource-constrained embedded systems. His most prominent contribution is the development of CNNParted, an open-source framework that enables efficient inference partitioning across heterogeneous hardware. This work, published in 2023 and already garnering 11 citations, addresses a critical bottleneck in edge AI: balancing computational demands with limited power and memory. By providing a modular, scalable solution for splitting neural network execution between devices like CPUs, GPUs, and specialized accelerators, Sidorenko’s research empowers developers to deploy complex models in real-world applications such as autonomous drones, smart sensors, and medical wearables. His focus on open-source tools underscores a commitment to democratizing AI, making cutting-edge optimization accessible to both academia and industry. With a growing citation footprint and a clear trajectory toward practical, energy-efficient AI, Sidorenko is shaping the future of embedded intelligence—where performance meets pragmatism.
Research Focus
Key Achievements
Top Papers
- 1