About Kamilya Smagulova Kamilya Smagulova Postdoctoral Research Fellow (former), Communication and Computing Systems Lab machine learning memristors neural network accelerator Education and early career Kamilya received her bachelor degree in Radio Engineering and Telecommunications from Almaty University of Power Engineering and Telecommunications (2008) and masters degree in Nanotechnology from University College London (2011). In 2021 she obtained her PhD degree in Electrical and Computer Engineering from Nazarbayev University. Currently Kamilya is a postdoctoral fellow at Communication and Computing Systems Lab (CCSL) under supervision of Prof. Ahmed Eltawil. Her research interests lie primarily in the area of machine learning and resistive hardware accelerators Projects Related Projects 2025 Efficient AI Across Edge, Near-Edge, and Cloud Thu, Sep 25 2025 Research AI accelerator Modern applications like smart cameras, self-driving cars, and VR devices rely on powerful AI models. Running these models quickly and efficiently across phones, edge devices, and cloud servers is a tough challenge. Our work develops two frameworks to make this possible: DONNA finds the best way to split and run AI models across different types of devices, from traditional CPUs and GPUs to new Compute-In-Memory (CIM) accelerators, so they use less energy while staying fast. HiDist takes the idea further by looking at the whole system: edge devices near the user, stronger near-edge servers, and
Efficient AI Across Edge, Near-Edge, and Cloud Thu, Sep 25 2025 Research AI accelerator Modern applications like smart cameras, self-driving cars, and VR devices rely on powerful AI models. Running these models quickly and efficiently across phones, edge devices, and cloud servers is a tough challenge. Our work develops two frameworks to make this possible: DONNA finds the best way to split and run AI models across different types of devices, from traditional CPUs and GPUs to new Compute-In-Memory (CIM) accelerators, so they use less energy while staying fast. HiDist takes the idea further by looking at the whole system: edge devices near the user, stronger near-edge servers, and
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