Akrit Mudvari
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SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization Open
Large language models (LLMs) have been a disruptive innovation in recent years, and they play a crucial role in our daily lives due to their ability to understand and generate human-like text. Their capabilities include natural language un…
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency Open
The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, im…
Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach Open
The practical realization of quantum programs that require large-scale qubit systems is hindered by current technological limitations. Distributed Quantum Computing (DQC) presents a viable path to scalability by interconnecting multiple Qu…
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach Open
Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands and denser deployments to meet varied service demands. This…
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN Open
In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a critical role in achieving a balance between centralized contr…
Joint SDN Synchronization and Controller Placement in Wireless Networks using Deep Reinforcement Learning Open
Software Defined Networking has afforded numerous benefits to the network users but there are certain persisting issues with this technology, two of which are scalability and privacy. The natural solution to overcoming these limitations is…
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency Open
The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, im…
Brownian Motion of Radioactive Particles: Derivation and Monte Carlo Test of Spatial and Temporal Distributions Open
Stochastic processes such as diffusion can be analyzed by means of a partial differential equation of the Fokker-Planck type (FPE), which yields a transition probability density, or by a stochastic differential equation of the Langevin typ…