Despite the effort of analog circuit design automation, currently complex analog circuit design still requires extensive manual iterations, making it labor intensive and time-consuming. Recently, reinforcement learning (RL) algorithms have been demonstrated successfully for the analog circuit design optimization. However, a robust and highly efficient RL method to design analog circuits with complex design space has not been fully explored yet. In this work, inspired by multiagent planning theory as well as human expert design practice, we propose a multiagent based RL (MA-RL) framework to tackle this issue. Particularly, we (i) partition the complex analog circuits into several sub-blocks based on topology information and effectively reduce the complexity of design search space; (ii) leverage MA-RL for the circuit optimization, where each agent corresponds to a single sub-block, and the interactions between agents delicately mimic the best design tradeoffs between circuit sub-blocks by human experts; (iii) introduce and compare three different multiagent RL algorithms and corresponding frameworks to demonstrate the effectiveness of the MA-RL method. (iv) employing twin-delayed techniques and proximal policy to further boost training stability and accomplish higher performances. (v) The impacts of different reward function definitions as well as different state settings of MA-RL agents are investigated to further improve the robustness of this framework. (vi) Experiments on three different complex analog circuit topologies (GBA, DLL and SAR ADC) and knowledge transfers between two technology nodes are demonstrated. It’s shown that MA-RL framework can achieve the best FoM for complex analog circuits’ design. This work shines the light for future large scale analog circuit system design automation.

Despite the effort of analog circuit design automation, currently complex analog circuit design still requires extensive manual iterations, making it labor intensive and time-consuming. Recently, reinforcement learning (RL) algorithms have been demonstrated successfully for the analog circuit design optimization. However, a robust and highly efficient RL method to design analog circuits with complex design space has not been fully explored yet. In this work, inspired by multiagent planning theory as well as human expert design practice, we propose a multiagent based RL (MA-RL) framework to tackle this issue. Particularly, we (i) partition the complex analog circuits into several sub-blocks based on topology information and effectively reduce the complexity of design search space; (ii) leverage MA-RL for the circuit optimization, where each agent corresponds to a single sub-block, and the interactions between agents delicately mimic the best design tradeoffs between circuit sub-blocks by human experts; (iii) introduce the multiagent twin-delayed techniques to further boost training stability and accomplish higher performances. Experiments on two different analog circuit topologies and knowledge transfers between two technology nodes are demonstrated. It’s shown that MA-RL framework can achieve the best FoM for complex analog circuits design. This work shines the light for future large scale analog circuit system design automation.

3D craniofacial model reconstruction from skull data has been a challenging research topic for many years. In this paper we develop a craniofacial reconstruction system based on feature points, and our approach only requires the skull model from a standard 3D scanner and feature points superimposed on the skull data. Our method consists of three stages: hole repairing for the 3D skull model, facial reconstruction with Radial Basis Function(RBF) deformation and texture mapping. A new advancing layer-wise solution algorithm and a template matching method are proposed for repairing big and particular holes on the skull model. The reference face model is then transformed to reconstruct the face model base on marked feature points on the skull model, and we improve the RBF deformation.. The experiments demonstrate the high efficiency and visual realism achieved in our approach.