Google Inc.

Shen Wang

James Laudon

Jeffrey Adgate Dean

Quoc V Le

Chian-Min Richard Ho

Azalia Mirhoseini

Anna Darling Goldie

Wenjie Jiang

Mustafa Nazim Yazgan

Roger David Carpenter

Young-Joon Lee

Ebrahim Songhori

Omkar Pathak

Kavya Srinivasa Setty

Radu Jurca

Chiu Wah Kelvin So

Mahesh Keralapura Manjunatha

Sudip Roy

Hanxiao Liu

Qiumin Xu

Yanqi Zhou

Amirali Abdolrashidi

Daniel Lin-Kit Wong

Xi Xiong

Chao Ma

Phitchaya Mangpo Phothilimthana

Muruo Liu

Vinay Vyas Vemuri

Andrei Dragus

Yao Liu

David Fan

Daniel Svonava

Md Mahbubul Hasan

Jakub Ocwieja

Aspects of the disclosure are directed to automatically determining floor planning in chips, which factors in memory macro alignment. A deep reinforcement learning (RL) agent can be trained to determine optimal placements for the memory macros, where memory macro alignment can be included as a regularization cost to be added to the placement objective as a RL reward. Tradeoffs between the placement objective and alignment of macros can be controlled by a tunable alignment parameter.

Alignment cost for integrated circuit placement

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compiler optimizations using a compiler optimization network. One of the methods includes receiving an input program, wherein the input program defines a graph of operation modules, wherein each node in the graph is a respective operation module, and each edge between nodes in the graph represents one operation module receiving the output generated by another operation module. The input program is processed by a compiler optimization network comprising a graph-embedding network that is configured to encode operation features and operation dependencies of the operation modules of the input program into a graph embedding representation and a policy network that is configured to generate an optimization action for each of one or more nodes encoded in the graph embedding representation. The compiler optimization network generates an output optimization plan comprising one or more optimization actions for the input program.

Learned graph optimizations for compilers

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Generating integrated circuit placements using neural networks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a content platform that receives a request to provide a digital component. The request includes information about users to which the digital component is directed. Futurized queries are obtained from serving logs of a serving system that is configured to execute an existing digital component using serving code that directs digital content to the set of users. The futurized queries are loaded as data structures in memory of a forecasting system. The system uses an instruction set derived from the serving code to determine that similarity between the particular futurized query and the request exceeds a threshold similarity. The system then generates a forecast output as a response to the request based on futurized queries that exceed the threshold similarity. The forecast output includes data describing future provision of the digital component.

Modeling distribution of digital components over a network

Growing community of inventors

Sunnyvale, CA, United States of America

Shen Wang