Google llc (20240160909). SHARED SCRATCHPAD MEMORY WITH PARALLEL LOAD-STORE simplified abstract
Contents
- 1 SHARED SCRATCHPAD MEMORY WITH PARALLEL LOAD-STORE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SHARED SCRATCHPAD MEMORY WITH PARALLEL LOAD-STORE - 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
SHARED SCRATCHPAD MEMORY WITH PARALLEL LOAD-STORE
Organization Name
Inventor(s)
Thomas Norrie of Mountain View CA (US)
Andrew Everett Phelps of Middleton WI (US)
Norman Paul Jouppi of Palo Alto CA (US)
Matthew Leever Hedlund of Sun Prairie WI (US)
SHARED SCRATCHPAD MEMORY WITH PARALLEL LOAD-STORE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160909 titled 'SHARED SCRATCHPAD MEMORY WITH PARALLEL LOAD-STORE
Simplified Explanation
The abstract describes a hardware circuit designed to implement a neural network, including a first memory, two processor cores, and a shared memory. The circuit is capable of performing computations for neural network layers using data provided by the first memory and storing vector values in the vector memories of the processor cores. The shared memory facilitates data routing between the first memory, processor cores, and vector registers.
- Hardware circuit for implementing a neural network
- Includes first memory, processor cores, and shared memory
- First memory provides data for computations
- Processor cores have vector memories for storing vector values
- Shared memory facilitates data routing between components
Potential Applications
The technology described in this patent application could be applied in various fields such as:
- Artificial intelligence
- Machine learning
- Data processing
- Neural network research
Problems Solved
The hardware circuit addresses several challenges in implementing neural networks, including:
- Efficient data routing
- Vector value storage
- Computation for neural network layers
Benefits
The benefits of this technology include:
- Improved performance in neural network computations
- Enhanced data processing capabilities
- Optimized memory usage
Potential Commercial Applications
This technology has potential commercial applications in industries such as:
- Technology
- Data analytics
- Robotics
- Autonomous systems
Possible Prior Art
One possible prior art for this technology could be:
- Previous hardware circuits for neural network implementation
Unanswered Questions
How does this technology compare to existing neural network hardware implementations?
This article does not provide a direct comparison to existing neural network hardware implementations.
What are the specific technical specifications of the hardware circuit described in the patent application?
The article does not delve into the specific technical specifications of the hardware circuit.
Original Abstract Submitted
methods, systems, and apparatus, including computer-readable media, are described for a hardware circuit configured to implement a neural network. the circuit includes a first memory, respective first and second processor cores, and a shared memory. the first memory provides data for performing computations to generate an output for a neural network layer. each of the first and second cores include a vector memory for storing vector values derived from the data provided by the first memory. the shared memory is disposed generally intermediate the first memory and at least one core and includes: i) a direct memory access (dma) data path configured to route data between the shared memory and the respective vector memories of the first and second cores and ii) a load-store data path configured to route data between the shared memory and respective vector registers of the first and second cores.
