Micron technology, inc. (20240281428). Energy Efficient Computations of Attention-based Inferences simplified abstract
Energy Efficient Computations of Attention-based Inferences
Organization Name
Inventor(s)
Saideep Tiku of Folsom CA (US)
Shashank Bangalore Lakshman of Folsom CA (US)
Energy Efficient Computations of Attention-based Inferences - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240281428 titled 'Energy Efficient Computations of Attention-based Inferences
Simplified Explanation
This patent application describes an apparatus that can compute an attention matrix using an attention mechanism in artificial neural networks. It involves storing key-value pairs in memory, reordering keys, computing dot products, generating attention scores, and ultimately creating an attention matrix.
Key Features and Innovation
- Memory to store key-value pairs
- Reorder buffer for reordering keys
- Analog dot product accelerator for computing dot products
- Processing device for generating attention scores
- Accelerator for computing dot products of attention scores with value elements to create an attention matrix
Potential Applications
This technology can be used in various fields such as natural language processing, image recognition, and recommendation systems where attention mechanisms are crucial for improving performance.
Problems Solved
This technology addresses the need for efficient computation of attention matrices in artificial neural networks, which is essential for enhancing the accuracy and performance of various AI applications.
Benefits
- Improved accuracy in AI applications
- Enhanced performance of neural networks
- Efficient computation of attention matrices
Commercial Applications
- Natural language processing systems
- Image recognition software
- Recommendation algorithms
Prior Art
Researchers can explore prior art related to attention mechanisms in artificial neural networks, dot product accelerators, and memory management in AI systems to understand the existing technologies in this field.
Frequently Updated Research
Researchers are continually exploring new techniques and optimizations for attention mechanisms in artificial neural networks, which can provide valuable insights for further advancements in this technology.
Questions about Attention Mechanism in Artificial Neural Networks
How does the apparatus compute attention scores based on dot products?
The apparatus computes attention scores by multiplying key elements with query elements and generating a row of attention scores corresponding to the query row of the query matrix.
What are the potential applications of this technology beyond artificial neural networks?
This technology can be applied in various fields such as natural language processing, image recognition, and recommendation systems to enhance performance and accuracy.
Original Abstract Submitted
an apparatus to compute an attention matrix implementing an attention mechanism in artificial neural networks, having: memory to store key value pairs; a reorder buffer to provide a reordered list of keys from the key value pairs; an analog dot product accelerator configured to compute dot products of key elements of keys from the reordered list of keys with respective query elements of a query row of a query matrix; a processing device configured to generate, based on results of the dot products, a row of attention scores corresponding to the query row of the query matrix for the reordered list of keys; and a further accelerator configured to compute dot products of segments of the attention scores with value elements of respective segments of values from a list of values from the key value pairs to generate an attention matrix.