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IEEE/ICACT20260329 Slide.09        [Big Slide]       [YouTube]	Oral Presentation
	To optimize this, we developed a cost model that minimizes per-iteration training time by balancing computation, communication, and pipeline bubbles. where stages accounts for both communication and computation times. Decision variables include layer partitioning and device mapping, but we must preserve privacy by not exposing internal DC details.

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