Samsung electronics co., ltd. (20240095518). STRUCTURED SPARSE MEMORY HIERARCHY FOR DEEP LEARNING simplified abstract
Contents
- 1 STRUCTURED SPARSE MEMORY HIERARCHY FOR DEEP LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 STRUCTURED SPARSE MEMORY HIERARCHY FOR DEEP LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
STRUCTURED SPARSE MEMORY HIERARCHY FOR DEEP LEARNING
Organization Name
Inventor(s)
Ardavan Pedram of Santa Clara CA (US)
Jong Hoon Shin of San Jose CA (US)
Joseph H. Hassoun of Los Gatos CA (US)
STRUCTURED SPARSE MEMORY HIERARCHY FOR DEEP LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095518 titled 'STRUCTURED SPARSE MEMORY HIERARCHY FOR DEEP LEARNING
Simplified Explanation
The patent application describes a memory system and method for training a neural network model by decompressing activation and weight tensors to predetermined sparsity densities before processing them in a neural processing unit.
- Decompressor unit decompresses activation and weight tensors to predetermined sparsity densities.
- Buffer unit receives the decompressed tensors at the predetermined sparsity densities.
- Neural processing unit computes results based on the sparsity densities of the tensors.
Potential Applications
This technology could be applied in various fields such as:
- Machine learning
- Artificial intelligence
- Data processing
Problems Solved
This technology helps in addressing the following issues:
- Efficient training of neural network models
- Optimizing memory usage in neural network processing
Benefits
The benefits of this technology include:
- Improved performance in neural network training
- Reduced memory overhead
- Enhanced efficiency in data processing
Potential Commercial Applications
The potential commercial applications of this technology could be seen in:
- Cloud computing services
- Data centers
- AI hardware development
Possible Prior Art
One possible prior art could be the use of compression techniques in neural network training to optimize memory usage and improve processing efficiency.
Unanswered Questions
How does this technology compare to existing methods of neural network training?
This article does not provide a direct comparison with existing methods of neural network training. It would be helpful to understand the specific advantages and disadvantages of this approach compared to traditional methods.
What are the potential limitations or drawbacks of this technology?
The article does not address any potential limitations or drawbacks of this technology. It would be important to consider any challenges or constraints that may arise in implementing this approach in practical applications.
Original Abstract Submitted
a memory system and a method are disclosed for training a neural network model. a decompressor unit decompresses an activation tensor to a first predetermined sparsity density based on the activation tensor being compressed, and decompresses an weight tensor to a second predetermined sparsity density based on the weight tensor being compressed. a buffer unit receives the activation tensor at the first predetermined sparsity density and the weight tensor at the second predetermined sparsity density. a neural processing unit receives the activation tensor and the weight tensor from the buffer unit and computes a result for the activation tensor and the weight tensor based on first predetermined sparsity density of the activation tensor and based on the second predetermined sparsity density of the weight tensor.
