Patent application EP21177007A (EP), filed 2021-06-01
Publication number: EP3923208A1
Title: AUTOMATED HARDWARE RESOURCE OPTIMIZATION
Abstract: An automated hardware resource optimization system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to identify computing hardware for hosting a neural network based application, determine, based on a first performance parameter of the computing hardware, a batch size for performing data processing using the neural network based application, and tune, using a data batch having the determined batch size, a second performance parameter of the computing hardware to enable substantially continuous loading of its hardware processor memory. The software code also optimizes, based on the determined batch size and the tuned second performance parameter, a process flow for performing the data processing, and generates a configuration file identifying the computing hardware, the neural network based application, the determined batch size, the tuned second performance parameter, and the optimized process flow.
IPC codes: G06F   9/    50            A I
G06N   3/    08            A I
Claims: number: 1
text: A method for use by an automated hardware resource optimization system, the method comprising:
number: 2
text: The method of claim 1, wherein the data processing comprises a plurality of tasks, and wherein the optimized process flow results in performance of at least some of the plurality of tasks concurrently.
number: 3
text: The method of any of claims 1-2, further comprising:
number: 4
text: The method of any of claims 1-3, wherein the first performance parameter of the computing hardware comprises at least one of a tensor processing unit (TPU) memory load, a graphics processing unit (GPU) memory load, or a central processing unit (CPU) memory load during processing of a test data.
number: 5
text: The method of any of claims 1-4, wherein the second performance parameter of the computing hardware comprises at least one of a TPU usage rate, a TPU memory load rate, a GPU usage rate, a GPU memory load rate, a CPU usage rate, or a CPU memory load rate during processing of the data batch having the determined batch size.
number: 6
text: The method of any of claims 1-5, wherein the neural network based application comprises a plurality of neural networks.
number: 7
text: The method of any of claims 1-6, wherein the computing hardware comprises a plurality of machines.
number: 8
text: The method of claim 7, wherein at least one of the plurality of machines is a cloud-based machine and at least another of the plurality of machines is part of a same local network as the automated hardware resource optimization system.
number: 9
text: The method of any of claims 1-6, wherein the computing hardware comprises at least one machine, and wherein the automated hardware resource optimization system and the at least one machine are part of a same local network.
number: 10
text: The method of any of claims 1-6, wherein the computing hardware comprises at least one cloud-based machine.
number: 11
text: An automated hardware resource optimization system comprising:
Office action: search_report, dated 2021-12-15
Prior art cited: ref_id: US2020042362A1
ref_type: patent
severity: background
ref_id: US2019266015A1
ref_type: patent
severity: background
ref_id: - JIANG WENBIN WENBINJIANG@HUST EDU CN ET AL: "Layup", ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, ASSOCIATION FOR COMPUTING MACHINERY, US, vol. 16, no. 4, 11 October 2019 (2019-10-11), pages 1 - 23, XP058450025, ISSN: 1544-3566, DOI: 10.1145/3357238
ref_type: npl
severity: background
ref_id: US2019266015
ref_type: patent
severity: background
ref_id: US2020042362
ref_type: patent
severity: background
ref_id: - JIANG WENBIN WENBINJIANG@HUST EDU CN ET AL, "Layup", ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, ASSOCIATION FOR COMPUTING MACHINERY, US, (20191011), vol. 16, no. 4, doi:10.1145/3357238, ISSN 1544-3566, pages 1 - 23, XP058450025 [A] 1-11 * paragraph  [05.1] *
ref_type: npl
severity: background
Final disposition: pending (2021-12-15)