Google llc (20240231667). Heterogeneous ML Accelerator Cluster with Flexible System Resource Balance simplified abstract

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 20240231667 titled 'Heterogeneous ML Accelerator Cluster with Flexible System Resource Balance

• • Simplified Explanation:**

The patent application discusses a system for machine learning accelerators that incorporates memory nodes connected by high-speed chip-to-chip interconnects, eliminating the need for remote processing units to expand memory and improve performance.

• • Key Features and Innovation:**
• Heterogeneous machine learning accelerator system with compute and memory nodes.
• Memory nodes added to machine learning accelerator clusters via chip-to-chip interconnects.
• Support for prefetch and intelligent compression to utilize low-cost memory effectively.
• Higher performance, simpler software stack, and lower cost achieved without remote processing units.
• Enables memory expansion and improved performance without degradation.

• • Potential Applications:**

This technology can be applied in various fields such as artificial intelligence, data analytics, image recognition, and natural language processing.

• • Problems Solved:**

The technology addresses the limitations of existing systems that require remote processing units for memory expansion, resulting in higher costs and complex software stacks.

• • Benefits:**
• Improved performance in machine learning tasks.
• Cost-effective memory expansion.
• Simplified software stack.
• Enhanced efficiency in utilizing low-cost memory.

• • Commercial Applications:**

The technology can be utilized in industries such as healthcare, finance, autonomous vehicles, and cybersecurity for faster and more efficient machine learning processes.

• • Prior Art:**

Researchers can explore prior art related to heterogeneous machine learning accelerators, memory expansion technologies, and chip-to-chip interconnects.

• • Frequently Updated Research:**

Stay updated on advancements in machine learning accelerator systems, memory node integration, and chip-to-chip interconnect technologies for potential improvements in performance and cost-effectiveness.

• • Questions about Machine Learning Accelerator Systems:**

1. How does the integration of memory nodes via chip-to-chip interconnects improve performance in machine learning tasks? 2. What are the potential cost savings associated with utilizing low-cost memory in machine learning accelerator clusters?

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.