18484790. LOW-PRECISION FLOATING-POINT DATAPATH IN A COMPUTER PROCESSOR simplified abstract (NVIDIA Corporation)

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 18484790 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 handling deep learning inference workloads. The mechanisms described in the abstract aim to provide energy-accuracy tradeoffs by utilizing 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.

• Energy-efficient floating-point data path micro-architectures with integer accumulation
• Enhanced mechanisms for per-vector scaled quantization (VS-Quant) of floating-point arguments

Potential Applications

The technology can be applied in various fields such as:

• Artificial intelligence
• Machine learning
• Data analytics

Problems Solved

The technology addresses the following issues:

• Energy consumption in deep learning inference workloads
• Accuracy of computations in deep learning tasks

Benefits

The benefits of this technology include:

• Improved energy efficiency in computer processors
• Enhanced accuracy in deep learning inference calculations

Potential Commercial Applications

The technology can be utilized in industries such as:

• Healthcare
• Finance
• Autonomous vehicles

Possible Prior Art

One possible prior art could be the use of specialized hardware accelerators for deep learning tasks to improve energy efficiency and performance.

What are the specific energy-accuracy tradeoffs provided by the mechanisms described in the patent application?

The specific energy-accuracy tradeoffs provided by the mechanisms include the use of 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. These tradeoffs aim to balance energy consumption with the accuracy of computations in deep learning inference tasks.

How do the mechanisms described in the patent application compare to existing solutions for improving energy efficiency in computer processors?

The mechanisms described in the patent application focus on leveraging the inherent resiliency of deep learning inference workloads to enhance energy efficiency. By utilizing energy-efficient floating-point data path micro-architectures with integer accumulation and per-vector scaled quantization, the technology offers a unique approach to optimizing energy consumption in processors handling deep learning tasks.

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.