Nvidia corporation (20240160406). LOW-PRECISION FLOATING-POINT DATAPATH IN A COMPUTER PROCESSOR simplified abstract

LOW-PRECISION FLOATING-POINT DATAPATH IN A COMPUTER PROCESSOR

Organization Name

nvidia corporation

Inventor(s)

Rangharajan Venkatesan of Sunnyvale CA (US)

Reena Elangovan of West Lafayette IN (US)

Charbel Sakr of ¿San Jose CA (US)

Brucek Kurdo Khailany of Rollingwood TX (US)

Ming Y Siu of Santa Clara CA (US)

Ilyas Elkin of Sunnyvale CA (US)

Brent Ralph Boswell of Aloha OR (US)

LOW-PRECISION FLOATING-POINT DATAPATH IN A COMPUTER PROCESSOR - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240160406 titled 'LOW-PRECISION FLOATING-POINT DATAPATH IN A COMPUTER PROCESSOR

Simplified Explanation

The patent application focuses on improving the energy efficiency of computer processors, such as graphics processing units, when running deep learning inference workloads. The mechanisms described in the abstract aim to leverage the resiliency of deep learning inference to enhance energy efficiency through various techniques, including energy-efficient floating-point data path micro-architectures with integer accumulation and per-vector scaled quantization of floating-point arguments.

• Energy-efficient floating-point data path micro-architectures with integer accumulation
• Per-vector scaled quantization (VS-quant) of floating-point arguments
• Energy-accuracy tradeoffs in deep learning inference calculations
• Leveraging the resiliency of deep learning inference workloads for improved energy efficiency

Potential Applications

This technology could be applied in various fields such as:

• Autonomous vehicles
• Healthcare diagnostics
• Natural language processing
• Robotics

Problems Solved

The technology addresses the following issues:

• High energy consumption in deep learning inference workloads
• Limited energy efficiency of computer processors
• Need for improved performance in energy-constrained environments

Benefits

The benefits of this technology include:

• Reduced energy consumption
• Improved energy efficiency
• Enhanced performance in deep learning inference tasks

Potential Commercial Applications

This technology has potential commercial applications in:

• Data centers
• Edge computing devices
• Mobile devices
• Internet of Things (IoT) devices

Possible Prior Art

One possible prior art related to this technology is the use of quantization techniques to improve the energy efficiency of deep learning inference workloads. Additionally, research on energy-efficient computing architectures for machine learning tasks may also be relevant.

What are the specific quantization techniques used in this technology?

The specific quantization techniques used in this technology include per-vector scaled quantization (VS-quant) of floating-point arguments.

How do the energy-efficient floating-point data path micro-architectures with integer accumulation contribute to improving energy efficiency?

The energy-efficient floating-point data path micro-architectures with integer accumulation help reduce energy consumption by optimizing the computation of deep learning inference calculations through efficient data processing techniques.

Original Abstract Submitted

mechanisms to exploit the inherent resiliency of deep learning inference workloads to improve the energy efficiency of computer processors such as graphics processing units with these workloads. the mechanisms provide energy-accuracy tradeoffs in the computation of deep learning inference calculations via energy-efficient floating point data path micro-architectures with integer accumulation, and enhanced mechanisms for per-vector scaled quantization (vs-quant) of floating-point arguments.