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Learning to translate between real world and simulated 3D sensors while transferring task models
(2019)
Learning-based vision tasks are usually specialized on the sensor technology for which data has been labeled. The knowledge of a learned model is simply useless when it comes to data which differs from the data on which the model has been initially trained or if the model should be applied to a totally different imaging or sensor source. New labeled data has to be acquired on which a new model can be trained. Depending on the sensor, this can even get more complicated when the sensor data becomes more abstract and hard to be interpreted and labeled by humans. To enable reuse of models trained for a specific task across different sensors minimizes the data acquisition effort. Therefore, this work focuses on learning sensor models and translating between them, thus aiming for sensor interoperability. We show that even for the complex task of human pose estimation from 3D depth data recorded with different sensors, i.e. a simulated and a Kinect 2TM depth sensor, human pose estimation can greatly improve by translating between sensor models without modifying the original task model. This process especially benefits sensors and applications for which labels and models are difficult if at all possible to retrieve from raw sensor data.
This paper presents a machine learning powered, procedural sizing methodology based on pre-computed look-up tables containing operating point characteristics of primitive devices. Several Neural Networks are trained for 90nm and 45nm technologies, mapping different electrical parameters to the corresponding dimensions of a primitive device. This transforms the geometric sizing problem into the domain of circuit design experts, where the desired electrical characteristics are now inputs to the model. Analog building blocks or entire circuits are expressed as a sequence of model evaluations, capturing the sizing strategy and intention of the designer in a procedure, which is reusable across different technology nodes. The methodology is employed for the sizing of two operational amplifiers, and evaluated for two technology nodes, showing the versatility and efficiency of this approach.
There is still a great reliance on human expert knowledge during the analog integrated circuit sizing design phase due to its complexity and scale, with the result that there is a very low level of automation associated with it. Current research shows that reinforcement learning is a promising approach for addressing this issue. Similarly, it has been shown that the convergence of conventional optimization approaches can be improved by transforming the design space from the geometrical domain into the electrical domain. Here, this design space transformation is employed as an alternative action space for deep reinforcement learning agents. The presented approach is based entirely on reinforcement learning, whereby agents are trained in the craft of analog circuit sizing without explicit expert guidance. After training and evaluating agents on circuits of varying complexity, their behavior when confronted with a different technology, is examined, showing the applicability, feasibility as well as transferability of this approach.
