View source for 18152428. Heterogeneous ML Accelerator Cluster with Flexible System Resource Balance simplified abstract (GOOGLE LLC)

=Heterogeneous ML Accelerator Cluster with Flexible System Resource Balance=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Sheng Li of Cupertino CA (US)|Sheng Li of Cupertino CA (US)]][[Category:Sheng Li of Cupertino CA (US)]]

[[:Category:Sridhar Lakshmanamurthy of Sunnyvale CA (US)|Sridhar Lakshmanamurthy of Sunnyvale CA (US)]][[Category:Sridhar Lakshmanamurthy of Sunnyvale CA (US)]]

[[:Category:Norman Paul Jouppi of Palo Alto CA (US)|Norman Paul Jouppi of Palo Alto CA (US)]][[Category:Norman Paul Jouppi of Palo Alto CA (US)]]

[[:Category:Martin Guy Dixon of Portland OR (US)|Martin Guy Dixon of Portland OR (US)]][[Category:Martin Guy Dixon of Portland OR (US)]]

[[:Category:Daniel Stodolsky of Cambridge MA (US)|Daniel Stodolsky of Cambridge MA (US)]][[Category:Daniel Stodolsky of Cambridge MA (US)]]

[[:Category:Quoc V. Le of Sunnyvale CA (US)|Quoc V. Le of Sunnyvale CA (US)]][[Category:Quoc V. Le of Sunnyvale CA (US)]]

[[:Category:Liqun Cheng of Palo Alto CA (US)|Liqun Cheng of Palo Alto CA (US)]][[Category:Liqun Cheng of Palo Alto CA (US)]]

[[:Category:Erik Karl Norden of San Jose CA (US)|Erik Karl Norden of San Jose CA (US)]][[Category:Erik Karl Norden of San Jose CA (US)]]

[[:Category:Parthasarathy Ranganathan of San Jose CA (US)|Parthasarathy Ranganathan of San Jose CA (US)]][[Category:Parthasarathy Ranganathan of San Jose CA (US)]]

==Heterogeneous ML Accelerator Cluster with Flexible System Resource Balance - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18152428 titled 'Heterogeneous ML Accelerator Cluster with Flexible System Resource Balance

The abstract describes a patent application for a heterogeneous machine learning accelerator system that includes compute and memory nodes connected by high-speed chip-to-chip interconnects. This system allows for the addition of memory nodes into machine learning accelerator clusters via the chip-to-chip interconnects, without the need for remote processing units, resulting in higher performance, a simpler software stack, and potentially lower costs. The memory nodes support features such as prefetch and intelligent compression to enable the use of low-cost memory without sacrificing performance.

* Memory nodes added to machine learning accelerator clusters via chip-to-chip interconnects
* No need for remote processing units for memory expansion
* Higher performance, simpler software stack, potentially lower costs
* Memory nodes support prefetch and intelligent compression for efficient memory usage
* Enables use of low-cost memory without performance degradation

Potential Applications:
- Machine learning acceleration
- Data processing and analytics
- High-performance computing

Problems Solved:
- Simplifying memory expansion in machine learning accelerator systems
- Improving performance and reducing costs
- Enhancing memory efficiency with prefetch and compression capabilities

Benefits:
- Increased performance in machine learning tasks
- Simplified system architecture
- Potential cost savings with the use of low-cost memory

Commercial Applications:
- Cloud computing services
- AI and machine learning applications
- Data centers and server infrastructure

Questions about the technology:
1. How does the addition of memory nodes via chip-to-chip interconnects improve performance in machine learning tasks?
2. What are the potential cost-saving implications of using low-cost memory in this system?

Frequently Updated Research:
- Stay updated on advancements in heterogeneous machine learning accelerator systems and memory node technology for improved performance and efficiency.


==Original Abstract Submitted==

Aspects of the disclosure are directed to a heterogeneous machine learning accelerator system with compute and memory nodes connected by high speed chip-to-chip interconnects. While existing remote/disaggregated memory may require memory expansion via remote processing units, aspects of the disclosure add memory nodes into machine learning accelerator clusters via the chip-to-chip interconnects without needing assistance from remote processing units to achieve higher performance, simpler software stack, and/or lower cost. The memory nodes may support prefetch and intelligent compression to enable the use of low cost memory without performance degradation.



[[Category:G06F3/06]]
[[Category:CPC_G06F3/0647]]