3 papers
cs.DC2025
Range Asymmetric Numeral Systems-Based Lightweight Intermediate Feature Compression for Split Computing of Deep Neural Networks
Mingyu Sung, Suhwan Im, Vikas Palakonda +1
Split computing distributes deep neural network inference between resource-constrained edge devices and cloud servers but faces significant communication bottlenecks when transmitt…
cs.LG2025
Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing
Mingyu Sung, Vikas Palakonda, Suhwan Im +4
Large language models (LLMs) have achieved near-human performance across diverse reasoning tasks, yet their deployment on resource-constrained Internet-of-Things (IoT) devices rema…
cs.DC2025
Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression
Mingyu Sung, Suhwan Im, Daeho Bang +3
Modern DNNs often rely on edge-cloud model partitioning (MP), but widely used schemes fix shallow, static split points that underutilize edge compute and concentrate latency and en…