Samsung electronics co., ltd. (20240095505). HYBRID-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY simplified abstract
Contents
- 1 HYBRID-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HYBRID-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
HYBRID-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY
Organization Name
Inventor(s)
Jong Hoon Shin of San Jose CA (US)
Ardavan Pedram of Santa Clara CA (US)
Joseph Hassoun of Los Gatos CA (US)
HYBRID-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095505 titled 'HYBRID-SPARSE NPU WITH FINE-GRAINED STRUCTURED SPARSITY
Simplified Explanation
The patent application describes a neural processing unit that supports dual-sparsity modes, with a weight buffer storing weight values in either a structured weight sparsity arrangement or a random weight sparsity arrangement. A weight multiplexer array outputs weight values as first operand values based on the selected weight sparsity arrangement, while an activation buffer stores activation values and an activation multiplexer array outputs activation values as second operand values. A multiplier array then calculates product values for each operand value pair.
- Weight buffer stores weight values in structured or random sparsity arrangement
- Weight multiplexer array outputs weight values based on selected sparsity arrangement
- Activation buffer stores activation values
- Activation multiplexer array outputs activation values as second operand values
- Multiplier array calculates product values for each operand value pair
Potential Applications
This technology could be applied in:
- Neural network processing
- Machine learning algorithms
- Image and speech recognition systems
Problems Solved
This technology helps in:
- Optimizing neural network computations
- Improving efficiency in processing large datasets
- Reducing power consumption in AI systems
Benefits
The benefits of this technology include:
- Faster processing speeds
- Enhanced accuracy in computations
- Reduced energy consumption
Potential Commercial Applications
A potential commercial application for this technology could be in:
- AI hardware development
- Cloud computing services
- Autonomous vehicles technology
Possible Prior Art
One possible prior art for this technology could be:
- Previous neural processing units with single sparsity modes
Unanswered Questions
How does this technology compare to existing neural processing units in terms of performance and efficiency?
This article does not provide a direct comparison with existing neural processing units.
Are there any limitations or drawbacks to using this dual-sparsity mode neural processing unit?
This article does not mention any limitations or drawbacks associated with the use of this technology.
Original Abstract Submitted
a neural processing unit is disclosed that supports dual-sparsity modes. a weight buffer is configured to store weight values in an arrangement selected from a structured weight sparsity arrangement or a random weight sparsity arrangement. a weight multiplexer array is configured to output one or more weight values stored in the weight buffer as first operand values based on the selected weight sparsity arrangement. an activation buffer is configured to store activation values. an activation multiplexer array includes inputs to the activation multiplexer array that are coupled to the activation buffer, and is configured to output one or more activation values stored in the activation buffer as second operand values in which each respective second operand value and a corresponding first operand value forming an operand value pair. a multiplier array is configured to output a product value for each operand value pair.
