Nvidia corporation (20240354570). VECTOR CLUSTERED QUANTIZATION simplified abstract
Contents
- 1 VECTOR CLUSTERED QUANTIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 VECTOR CLUSTERED QUANTIZATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Vector Clustered Quantization
- 1.11 Original Abstract Submitted
VECTOR CLUSTERED QUANTIZATION
Organization Name
Inventor(s)
Reena Elangovan of Santa Clara CA (US)
Charbel Sakr of Mountain View CA (US)
Brucek Kurdo Khailany of Austin TX (US)
VECTOR CLUSTERED QUANTIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354570 titled 'VECTOR CLUSTERED QUANTIZATION
Simplified Explanation
The patent application discusses vector clustered quantization, a method to reduce the precision of vectors of parameters for energy-efficient acceleration of deep neural networks.
Key Features and Innovation
- Vector clustered quantization reduces the precision (bitwidth) of vectors of parameters.
- Vectors are mapped into clusters based on quantization errors, with each cluster associated with a different quantizer.
- Quantizers are optimized using the Lloyd-Max algorithm to minimize per-cluster quantization noise.
- The method involves two steps: vector clustering and per-cluster quantization.
- The process may be repeated before quantizing vectors for processing by a neural network model.
Potential Applications
The technology can be applied in various fields such as artificial intelligence, machine learning, and data processing.
Problems Solved
- Reducing the precision of vectors of parameters for energy-efficient acceleration of deep neural networks.
- Minimizing quantization noise in the clustering process.
Benefits
- Energy-efficient acceleration of deep neural networks.
- Improved processing efficiency of neural network models.
- Reduction in computational resources required for processing.
Commercial Applications
- The technology can be utilized in industries that heavily rely on deep neural networks, such as autonomous vehicles, healthcare, and finance, to improve performance and reduce energy consumption.
Questions about Vector Clustered Quantization
How does vector clustered quantization optimize quantizers for per-cluster quantization?
Vector clustered quantization optimizes quantizers using the Lloyd-Max algorithm to minimize per-cluster quantization noise.
What are the potential applications of vector clustered quantization technology?
The technology can be applied in various fields such as artificial intelligence, machine learning, and data processing.
Original Abstract Submitted
vector clustered quantization reduces precision (bitwidth) vectors of parameters and may enable energy-efficient acceleration of deep neural networks. a vector comprises one or more parameters within a single dimension of a multi-dimensional tensor (matrix or kernel). a set of quantizers is initialized for a first step (vector-clustering). after initialization, vectors are mapped into clusters based on quantization errors, where each one of the clusters is associated with a different one of the quantizers. during the second step (per-cluster quantization) each quantizer is optimized to quantize the vectors in the cluster that is associated with the quantizer. in an embodiment, the quantizers are optimized using the lloyd-max algorithm, which effectively minimizes the per-cluster quantization noise. the first and second steps may be repeated before the vectors are quantized for processing by a neural network model.
