768 research outputs found
(4R,11R)-9-(1-hydroxypropan-2-yl)-4,11-diphenyl-1,3,5,7,9-pentaazatricyclo[5.3.1.04,11]undecane-2,6-dithione
The asymmetric unit of the title compound, C21H23N5OS2, contains two independent chiral molecules. The two phenyl rings of one molecule form a dihedral angle of 51.95 (7)° and the distance between their centroids is 4.345 (1) Å. In the other molecule, the phenyl rings form a dihedral angle of 58.79 (8)° with a ring centroid–centroid distance of 4.435 (2) Å. An intramolecular O—H⋯N hydrogen bond occurs in each independent molecule. The crystal packing is stabilized by and intermolecular N—H⋯O and N—H⋯S hydrogen bonds and C—H⋯S interactions
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
Automatic transistor sizing is a challenging problem in circuit design due to
the large design space, complex performance trade-offs, and fast technological
advancements. Although there has been plenty of work on transistor sizing
targeting on one circuit, limited research has been done on transferring the
knowledge from one circuit to another to reduce the re-design overhead. In this
paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning
(RL) to transfer the knowledge between different technology nodes and
topologies. Moreover, inspired by the simple fact that circuit is a graph, we
learn on the circuit topology representation with graph convolutional neural
networks (GCN). The GCN-RL agent extracts features of the topology graph whose
vertices are transistors, edges are wires. Our learning-based optimization
consistently achieves the highest Figures of Merit (FoM) on four different
circuits compared with conventional black-box optimization methods (Bayesian
Optimization, Evolutionary Algorithms), random search, and human expert
designs. Experiments on transfer learning between five technology nodes and two
circuit topologies demonstrate that RL with transfer learning can achieve much
higher FoMs than methods without knowledge transfer. Our transferable
optimization method makes transistor sizing and design porting more effective
and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6
pages, 8 figure
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
A multitude of publicly-available driving datasets and data platforms have
been raised for autonomous vehicles (AV). However, the heterogeneities of
databases in size, structure and driving context make existing datasets
practically ineffective due to a lack of uniform frameworks and searchable
indexes. In order to overcome these limitations on existing public datasets,
this paper proposes a data unification framework based on traffic primitives
with ability to automatically unify and label heterogeneous traffic data. This
is achieved by two steps: 1) Carefully arrange raw multidimensional time series
driving data into a relational database and then 2) automatically extract
labeled and indexed traffic primitives from traffic data through a Bayesian
nonparametric learning method. Finally, we evaluate the effectiveness of our
developed framework using the collected real vehicle data.Comment: 6 pages, 7 figures, 1 table, ITSC 201
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