The widespread adoption of Mixture-of-Experts (MoE) has created a growing need for deployment on heterogeneous platforms. However, it exposes a fundamental mismatch between the algorithmic demands of large-scale MoE and the disparate characteristics of hardware.Existing CPU-GPU hybrid inference systems fail to resolve...
Wen-Xun Wang, Li-Kai Ma, Zong-Le Huang et al.· 0 citations
End-to-end case studies show that sparse workloads favor criticality-aware heterogeneous pods, whereas LLM inference favors scale-up islands; the resulting designs deliver 6.20 and 2.12 geomean speedups, respectively, while reducing cost and power relative to the baselines.
Yu-Chen Fan, Minghong Sun, Ji-Kui Ma et al.· arXiv.org· 0 citations
Diffusion models have shown marked advancements in 2-D generation and have also become focal points in 3-D generation via consistent multiview image generation. However, the computation and memory demands hinder their real-time deployment on mobile and edge devices. Moreover, the reduction of diffusion timesteps leads...
Wenxun Wang, Li-Kai Ma, Chen Tang et al.· IEEE Transactions on Compute...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.