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

GOOGLE LLC

Inventor(s)

Sheng Li of Cupertino CA (US)

Sridhar Lakshmanamurthy of Sunnyvale CA (US)

Norman Paul Jouppi of Palo Alto CA (US)

Martin Guy Dixon of Portland OR (US)

Daniel Stodolsky of Cambridge MA (US)

Quoc V. Le of Sunnyvale CA (US)

Liqun Cheng of Palo Alto CA (US)

Erik Karl Norden of San Jose CA (US)

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.