18538360. VARIABLE INPUT SHAPES AT RUNTIME simplified abstract (Imagination Technologies Limited)
Contents
- 1 VARIABLE INPUT SHAPES AT RUNTIME
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 VARIABLE INPUT SHAPES AT RUNTIME - 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 Dynamic Neural Network Hardware Implementation
- 1.13 Original Abstract Submitted
VARIABLE INPUT SHAPES AT RUNTIME
Organization Name
Imagination Technologies Limited
Inventor(s)
James Imber of Hertfordshire (GB)
Cagatay Dikici of Kings Langley (GB)
Biswarup Choudhury of Hertfordshire (GB)
VARIABLE INPUT SHAPES AT RUNTIME - A simplified explanation of the abstract
This abstract first appeared for US patent application 18538360 titled 'VARIABLE INPUT SHAPES AT RUNTIME
Simplified Explanation
The patent application describes a method for implementing a dynamic neural network in hardware to operate on input tensors of variable dimensions. This involves transforming the dynamic network into a static network that can handle fixed size inputs, and then implementing multiple instances of the static network in hardware to process overlapping fixed size inputs along the variable dimension of the input tensor.
- Receiving a representation of the dynamic neural network
- Transforming the dynamic network into a static network for fixed size inputs
- Implementing multiple instances of the static network in hardware to process overlapping fixed size inputs along the variable dimension of the input tensor
Key Features and Innovation
- Transformation of dynamic neural network into static network for fixed size inputs
- Implementation of multiple instances of static network in hardware for processing overlapping fixed size inputs
- Adaptation of operations to handle variable dimensions of input tensors
Potential Applications
- Image recognition
- Natural language processing
- Autonomous vehicles
- Robotics
- Medical diagnostics
Problems Solved
- Efficient processing of input tensors with variable dimensions
- Hardware implementation of dynamic neural networks
- Handling overlapping fixed size inputs along variable dimensions
Benefits
- Improved performance in processing input tensors
- Scalability for handling various input dimensions
- Hardware acceleration for dynamic neural networks
Commercial Applications
The technology can be applied in various industries such as healthcare, automotive, manufacturing, and finance for tasks like image analysis, speech recognition, predictive maintenance, and fraud detection.
Prior Art
Readers interested in prior art related to this technology can explore research papers, patents, and academic publications in the fields of neural networks, hardware acceleration, and machine learning.
Frequently Updated Research
Stay updated on advancements in hardware acceleration for neural networks, dynamic network transformations, and efficient processing of input tensors with variable dimensions.
Questions about Dynamic Neural Network Hardware Implementation
How does the transformation of a dynamic neural network into a static network benefit hardware implementation?
The transformation allows for efficient processing of fixed size inputs in hardware, enabling scalability and improved performance.
What are the potential challenges in implementing multiple instances of the static network for processing overlapping fixed size inputs?
One potential challenge could be optimizing the hardware resources to handle the parallel processing of multiple instances effectively.
Original Abstract Submitted
A method of implementing in hardware a dynamic neural network for operation on an input tensor having a variable dimension, the method including: receiving a representation of the dynamic neural network; transforming the representation of the dynamic neural network into a static network adapted to operate on a fixed size input, the static network being adapted to perform operations on the fixed size input which are equivalent to the operations performed by the dynamic neural network on its input tensor; and implementing a plurality of instances of the static network in hardware for operation on an input tensor split into a sequence of overlapping fixed size inputs along its variable dimension, each instance of the static network being arranged to operate on a respective fixed size input of the sequence.
