公开(公告)号：US20240220867A1

公开(公告)日：2024-07-04

申请号：US18289173

申请日：2021-05-10

Applicant: Google LLC

Inventor： Claudionor Jose Nunes Coelho, Jr. ,  Aki Oskari Kuusela ,  Satrajit Chatterjee ,  Piotr Zielinski ,  Hao Zhuang

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented on a computation graph. One of the methods comprises receiving data representing a neural network comprising a plurality of layers arranged in a sequence; selecting one or more groups of layers each comprising one or more layers adjacent to each other in the sequence; generating a new machine learning model, comprising: for each group of layers, a respective decision tree that replaces the group of layers, wherein the respective decision tree receives as input a quantized version of the inputs to a respective first layer in the group and generates as output a quantized version of the outputs of a respective last layer in the group, wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group.

公开(公告)号：US20230412821A1

公开(公告)日：2023-12-21

申请号：US18252158

申请日：2020-11-09

Applicant: Google LLC

Inventor： Daniele Moro ,  Claudionor Coelho ,  Sean R. Purser-Haskell ,  Hao Zhuang ,  Stan Vitvitskyy

IPC: H04N19/176 ,  H04N19/154 ,  G06T7/11 ,  G06V10/764 ,  G06V30/414

CPC classification number: H04N19/176 ,  H04N19/154 ,  G06V30/414 ,  G06V10/764 ,  G06T7/11

Abstract: This document describes systems and techniques for encoding a video frame using different compression ratios or compression algorithms for text blocks and non-text blocks. The described systems and techniques can determine, using a machine-learned model, which blocks of a frame include and do not include text. The described systems and techniques can then use a different compression ratio or compression algorithm for text blocks than the compression ratio or compression algorithm used for non-text blocks. For example, the systems and techniques can encode the text blocks using a first compression ratio that results in higher video quality than a second compression ratio used on at least some non-text blocks. In this way, the described systems and techniques can improve text legibility in a video file without significantly increasing the bandwidth requirements to transmit the video file to remote computing devices.

公开(公告)号：US20230229895A1

公开(公告)日：2023-07-20

申请号：US18007871

申请日：2021-06-02

Applicant: Google LLC

Inventor： Claudionor Jose Nunes Coelho, Jr. ,  Piotr Zielinski ,  Aki Kuusela ,  Shan Li ,  Hao Zhuang

IPC: G06N3/0495 ,  G06N3/092

CPC classification number: G06N3/0495 ,  G06N3/092

Abstract: Systems and methods for producing a neural network architecture with improved energy consumption and performance tradeoffs are disclosed, such as would be deployed for use on mobile or other resource-constrained devices. In particular, the present disclosure provides systems and methods for searching a network search space for joint optimization of a size of a layer of a reference neural network model (e.g., the number of filters in a convolutional layer or the number of output units in a dense layer) and of the quantization of values within the layer. By defining the search space to correspond to the architecture of a reference neural network model, examples of the disclosed network architecture search can optimize models of arbitrary complexity. The resulting neural network models are able to be run using relatively fewer computing resources (e.g., less processing power, less memory usage, less power consumption, etc.), all while remaining competitive with or even exceeding the performance (e.g., accuracy) of current state-of-the-art, mobile-optimized models.

公开(公告)号：US20240232594A1

公开(公告)日：2024-07-11

申请号：US18289292

申请日：2021-05-03

Applicant: Google LLC

Inventor： Yang Yang ,  Claudionor Jose Nunes Coelho, Jr. ,  Hao Zhuang ,  Aki Oskari Kuusela

IPC: G06N3/063 ,  G06N3/0464 ,  G06N3/082

CPC classification number: G06N3/063 ,  G06N3/0464 ,  G06N3/082

Abstract: Methods, systems, and apparatus, including computer-readable media, are described for globally tuning and generating ML hardware accelerators. A design system selects an architecture representing a baseline processor configuration. An ML cost model of the system generates performance data about the architecture at least by modeling how the architecture executes computations of a neural network that includes multiple layers. Based on the performance data, the architecture is dynamically tuned to satisfy a performance objective when the architecture implements the neural network and executes machine-learning computations for a target application. In response to dynamically tuning the architecture, the system generates a configuration of an ML accelerator that specifies customized hardware configurations for implementing each of the multiple layers of the neural network.