Treatment of stage I seminoma

Naylor, Sharon

doi:10.1186/isrctn54221666

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used a conjugate gradient penalty function-based NLP solver package [15] in the multilevel framework. The problem formulation based on the resistive model in Section 3.3 (P) is solved at every refinement level with an initial solution provided by ADVP and proper TS-via number adjustment.…”

Section: Resultsmentioning

confidence: 99%

Thermal via planning for 3-D ICs

Cong¹,

Zhang²

ICCAD-2005. IEEE/ACM International Conference on Computer-Aided Design, 2005.

106

130

View full text Add to dashboard Cite

Heat dissipation is one of the most serious challenges in 3-D IC designs. One effective way of reducing circuit temperature is to introduce thermal through-the-silicon (TTS) vias. In this paper, we extended the TTS-via planning in a multilevel routing framework as in [7], but use a much enhanced TTS-via planning algorithm. We formulate the TTSvia minimization problem with temperature constraints as a constrained nonlinear programming problem (NLP) based on the thermal resistive model and develop an efficient heuristic algorithm, named m-ADVP, which solves a sequence of simplified via planning subproblems in alternating direction in a multilevel framework. The vertical via distribution is formulated as a convex programming problem, and the horizontal via planning is based on two efficient techniques: path counting and heat propagation. Experimental results show that the m-ADVP algorithm is more than 200× faster than the direct solution to the NPL formulation for via planning with very similar solution quality (within 1% of TS-vias count). However, compared to a recent work of multilevel TS-via planning algorithm based on temperature profiling [7], our algorithm can reduce the total TS-via number by over 68% for the same required temperature with similar runtime.

show abstract

Section: Resultsmentioning

confidence: 99%