公开(公告)号：US20230306272A1

公开(公告)日：2023-09-28

申请号：US18185031

申请日：2023-03-16

Applicant: Micron Technology, Inc.

Inventor： Andre Xian Ming Chang ,  Abhishek Chaurasia ,  Parth Khopkar ,  Bashar Romanous ,  Patrick Alan Estep ,  Skyler Arron Windh ,  Eugenio Culurciello ,  Sheik Dawood Beer Mohideen

IPC: G06N3/092 ,  G06N3/045 ,  G06N3/10

CPC classification number: G06N3/092 ,  G06N3/045 ,  G06N3/10

Abstract: An artificial neural network is trained via reinforcement learning to receive first data representative of execution dependency conditions of instructions of a program, second data representative of a schedule of a first portion of the instructions of the program for execution in a device having a plurality of circuits units operable in parallel, and third data identifying a next instruction selected from a second portion of the instructions of the program remaining to be scheduled for execution in the device. The artificial neural network selects a placement of the next instruction in one of the circuit units from a plurality of possible placements of the next instruction in the device. Performance of placements of instructions being tested in search for a valid schedule for running the program in the device can be measured to generate samples to train the artificial neural network via reinforcement learning.

公开(公告)号：US20240257531A1

公开(公告)日：2024-08-01

申请号：US18420489

申请日：2024-01-23

Applicant: Micron Technology, Inc.

Inventor： Parth Khopkar ,  Shakti Nagnath Wadekar ,  Abhishek Chaurasia ,  Andre Xian Ming Chang

IPC: G06V20/58 ,  G06V10/82

CPC classification number: G06V20/58 ,  G06V10/82

Abstract: Methods, systems, and devices for techniques to implement transformers with multi-task neural networks are described. A vehicle system may employ one or more transformer models in a machine learning system to generate an indication of a one or more objects in an image, one or more drivable areas in an image, one or more lane lines in an image, or a combination thereof. The multi-task system may include a feature extractor which uses a set of convolutional layers to generate a corresponding set of representation vectors of the image. The system may pass the representation vectors to a set of transformer models, such that each of the transformer models share a common input. Each transformer model may use the representation vectors to generate a respective indication.