Google llc (20240220773). YIELD IMPROVEMENTS FOR THREE-DIMENSIONALLY STACKED NEURAL NETWORK ACCELERATORS simplified abstract
Contents
- 1 YIELD IMPROVEMENTS FOR THREE-DIMENSIONALLY STACKED NEURAL NETWORK ACCELERATORS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 YIELD IMPROVEMENTS FOR THREE-DIMENSIONALLY STACKED NEURAL NETWORK ACCELERATORS - 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 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Three-Dimensionally Stacked Neural Network Accelerators
- 1.13 Original Abstract Submitted
YIELD IMPROVEMENTS FOR THREE-DIMENSIONALLY STACKED NEURAL NETWORK ACCELERATORS
Organization Name
Inventor(s)
Andreas Georg Nowatzyk of San Jose CA (US)
YIELD IMPROVEMENTS FOR THREE-DIMENSIONALLY STACKED NEURAL NETWORK ACCELERATORS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240220773 titled 'YIELD IMPROVEMENTS FOR THREE-DIMENSIONALLY STACKED NEURAL NETWORK ACCELERATORS
Simplified Explanation
The patent application describes methods, systems, and apparatus for three-dimensionally stacked neural network accelerators. These accelerators include multiple neural network dies with tiles, and the method involves rerouting dataflow configurations to bypass faulty tiles.
- Three-dimensionally stacked neural network accelerators
- Neural network dies with tiles
- Rerouting dataflow configurations to bypass faulty tiles
Key Features and Innovation
- Three-dimensionally stacked neural network accelerators with multiple neural network dies
- Ability to identify and bypass faulty tiles in the dataflow configuration
- Efficient processing of inputs by rerouting dataflow configurations
Potential Applications
- Artificial intelligence
- Machine learning
- Neural network processing tasks
Problems Solved
- Addressing faults in neural network accelerators
- Improving the reliability and efficiency of data processing
Benefits
- Enhanced performance of neural network accelerators
- Increased reliability in data processing
- Improved overall efficiency in neural network tasks
Commercial Applications
Neural network accelerators with fault-tolerant features can be used in various industries such as healthcare, finance, and autonomous vehicles for faster and more reliable data processing.
Prior Art
Readers can explore prior research on fault-tolerant neural network accelerators, dataflow configurations, and three-dimensionally stacked architectures in the field of artificial intelligence and machine learning.
Frequently Updated Research
Stay updated on the latest advancements in fault-tolerant neural network accelerators, data processing efficiency, and three-dimensional stacking technologies for neural networks.
Questions about Three-Dimensionally Stacked Neural Network Accelerators
What are the potential drawbacks of using three-dimensionally stacked neural network accelerators?
Three-dimensionally stacked neural network accelerators may face challenges related to heat dissipation, power consumption, and manufacturing complexity.
How do three-dimensionally stacked neural network accelerators compare to traditional neural network architectures in terms of performance and efficiency?
Three-dimensionally stacked neural network accelerators offer improved performance and efficiency due to their compact design and optimized dataflow configurations.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for three-dimensionally stacked neural network accelerators. in one aspect, a method includes obtaining data specifying that a tile from a plurality of tiles in a three-dimensionally stacked neural network accelerator is a faulty tile. the three-dimensionally stacked neural network accelerator includes a plurality of neural network dies, each neural network die including a respective plurality of tiles, each tile has input and output connections. the three-dimensionally stacked neural network accelerator is configured to process inputs by routing the input through each of the plurality of tiles according to a dataflow configuration and modifying the dataflow configuration to route an output of a tile before the faulty tile in the dataflow configuration to an input connection of a tile that is positioned above or below the faulty tile on a different neural network die than the faulty tile.
