67 citations · 79 across the 6 of their papers we have counts for
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cs.LG2025★ 1 cited
LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers
Enda Yu, Dezun Dong, Zhaoning Zhang +6
Mixture-of-Experts (MoE) models face memory and PCIe latency bottlenecks when deployed on commodity hardware. Offloading expert weights to CPU memory results in PCIe transfer laten…
cs.LG2021★ 1 cited
CD-SGD: Distributed Stochastic Gradient Descent with Compression and Delay Compensation
Enda Yu, Dezun Dong, Yemao Xu +2
Communication overhead is the key challenge for distributed training. Gradient compression is a widely used approach to reduce communication traffic. When combining with parallel c…
cs.LG2020★ 1 cited
OD-SGD: One-step Delay Stochastic Gradient Descent for Distributed Training
Yemao Xu, Dezun Dong, Weixia Xu +1
The training of modern deep learning neural network calls for large amounts of computation, which is often provided by GPUs or other specific accelerators. To scale out to achieve…