Abstract:
A neural network (NN) performs macro placement on a chip. A mask is updated to mark invalid regions occupied by already-placed macros on a chip canvas. A policy network of the NN generates summary statistics of a two-dimensional (2D) continuous probability distribution over a continuous action space for a given state of the chip canvas. The NN selects an action based on the continuous probability distribution. The selected action corresponds to a coordinate in an unmasked region. The NN generates a trajectory including (state, action) pairs. The final state in the trajectory corresponds to a completed placement of macros.
Abstract:
Macros are placed on a canvas based on density map calculations. First, a grid representing the canvas is initialized. The grid is formed by grid cells. To place a macro on the grid, a coordinate on the grid is chosen, and multiple density maps are calculated using average-pooling filters of multiple resolutions or orientations. The lowest-level density map is described by the grid with each grid cell having a corresponding density value. The density value in a higher-level density map is calculated by performing an average-pooling operation on density values in a lower- level density map. The placement of the macro at the coordinate is validated when no density value in the density maps exceeds a corresponding density threshold.
Abstract:
A virtual reality (VR) system including a virtual reality display and a virtual reality host is provided. The virtual reality display is arranged for displaying a virtual environment for a virtual reality user. The virtual reality host is arranged for performing a virtual reality session to generate the virtual environment using the virtual reality display and creating a virtual interface to sync and interact with a source unit, wherein when the source unit receives an incoming event, the virtual reality host receives a notification regarding the incoming event from the source unit and provides the notification to the virtual interface to generate an alert for the notification to the screen of the virtual reality display in the virtual environment for the virtual reality user.
Abstract:
A virtual reality (VR) system including a virtual reality display and a virtual reality host is provided. The virtual reality display is arranged for displaying a virtual environment for a virtual reality user. The virtual reality host is arranged for performing a virtual reality session to generate the virtual environment using the virtual reality display and creating a virtual interface to sync and interact with a source unit, wherein when the source unit receives an incoming event, the virtual reality host receives a notification regarding the incoming event from the source unit and provides the notification to the virtual interface to generate an alert for the notification to the screen of the virtual reality display in the virtual environment for the virtual reality user.
Abstract:
A system trains a neural network (NN) for macro placement. The system constructs a set of positive samples of trajectories by sequentially removing the same set of macros in different orders from an at least partially-placed canvas of a chip. The system also constructs a set of negative samples of trajectories by placing not-yet-placed macros at random positions on an at least partially-empty canvas of the chip. The system then trains the NN and a graph NN (GNN) in the NN using the positive samples and the negative samples.
Abstract:
A system uses a neural network (NN) for macro placement. The system receives an input including objectives and a subspace of preferences. Each preference is a vector of weights assigned to corresponding objectives, and each objective is a measurement of a placement characteristic. The system trains the NN to place macros on a training set of chips to optimize a reward, where the reward is calculated from the objectives and the preferences. The NN generates a probability distribution of an action under a current state of a chip, where the action indicates a coordinate on the chip to place a macro. The NN further generates a sequence of (state, action) pairs to form a trajectory. The final state in the trajectory corresponds to a completed macro placement.