Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks
This addresses the bottleneck of slow thermal and IR drop analysis for IC designers, offering a significant speed-up over commercial tools, though it is incremental as it applies existing neural network methods to a specific domain problem.
The paper tackles the problem of computationally expensive temperature and IR drop analyses in IC design by using encoder-decoder networks to map these analyses to fast image-to-image and sequence-to-sequence translation tasks, achieving rapid predictions in milliseconds with average errors of 0.6% and 0.008% respectively.
Computationally expensive temperature and power grid analyses are required during the design cycle to guide IC design. This paper employs encoder-decoder based generative (EDGe) networks to map these analyses to fast and accurate image-to-image and sequence-to-sequence translation tasks. The network takes a power map as input and outputs the corresponding temperature or IR drop map. We propose two networks: (i) ThermEDGe: a static and dynamic full-chip temperature estimator and (ii) IREDGe: a full-chip static IR drop predictor based on input power, power grid distribution, and power pad distribution patterns. The models are design-independent and must be trained just once for a particular technology and packaging solution. ThermEDGe and IREDGe are demonstrated to rapidly predict the on-chip temperature and IR drop contours in milliseconds (in contrast with commercial tools that require several hours or more) and provide an average error of 0.6% and 0.008% respectively.