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Rahim Tafazolli

2 accepted papers

2025

FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training

NeurIPS 2025poster

The parameter size of modern large language models (LLMs) can be scaled up to the trillion-level via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has…

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2016

Uniform expected likelihood solution for interference rejection combining regularization

ICASSP 2016accepted

A well known problem of regularization (diagonal loading) of the interference rejection combining (IRC) and IRC / maximum ratio combining (MRC) switching is addressed. Different empirical loading factor selection rules adjusted to specific scenarios have been introduced in the literature. It is expe…

Cited by 0Source ↗Scholar ↗