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With the continuous drive towards integrated circuits scaling, efficient performance modeling is becoming more crucial yet, more challenging. In this paper, we propose a novel method of hierarchical performance modeling based on Bayesian co-learning. We exploit the hierarchical structure of a circuit to establish a Bayesian framework where unlabeled data samples are generated to improve modeling accuracy...
Efficient performance modeling of today’s analog and mixed-signal circuits is an important yet challenging task, due to the high-dimensional variation space and expensive circuit simulation. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian model fusion (BMF) to address this challenge. The key idea of BMF is to borrow the information collected from an...
In this paper, we propose a novel Dual-Prior Bayesian Model Fusion (DP-BMF) algorithm for performance modeling. Different from the previous BMF methods which use only one source of prior knowledge, DP-BMF takes advantage of multiple sources of prior knowledge to fully exploit the available information and, hence, further reduce the modeling cost. Based on a graphical model, an efficient Bayesian inference...
Tunable circuit has emerged as a promising methodology to address the grand challenge posed by process variations. Efficient high-dimensional performance modeling of tunable analog/RF circuits is an important yet challenging task. In this paper, we propose a novel performance modeling approach for tunable circuits, referred to as Correlated Bayesian Model Fusion (C-BMF). The key idea is to encode...
Efficient performance modeling of today's analog and mixed-signal (AMS) circuits is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Co-Learning Bayesian Model Fusion (CL-BMF). The key idea of CL-BMF is to take advantage of the additional information collected from simulation and/or measurement to reduce the performance modeling...
Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF). Our key idea is to borrow the simulation data generated from an early stage (e.g., schematic level) to...
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