4 papers
Bandwidth-Efficient Adaptive Mixture-of-Experts via Low-Rank Compensation
Zhenyu Liu, Yunzhen Liu, Zehao Fan +5
Mixture-of-Experts (MoE) models scale capacity via sparse activation but stress memory and bandwidth. Offloading alleviates GPU memory by fetching experts on demand, yet token-leve…
Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors
Wencheng Zou, Nan Wu
Graph neural networks (GNNs) have been widely applied in safety-critical applications, such as financial and medical networks, in which compromised predictions may cause catastroph…
A Benchmark on Directed Graph Representation Learning in Hardware Designs
Haoyu Wang, Yinan Huang, Nan Wu +1
To keep pace with the rapid advancements in design complexity within modern computing systems, directed graph representation learning (DGRL) has become crucial, particularly for en…
Systematic Literature Review of AI-enabled Spectrum Management in 6G and Future Networks
Bushra Sabir, Shuiqiao Yang, David Nguyen +7
Artificial Intelligence (AI) has advanced significantly in various domains like healthcare, finance, and cybersecurity, with successes such as DeepMind's medical imaging and Tesla'…