Google llc (20240211458). Efficient Embedding Table Storage and Lookup simplified abstract
Contents
Efficient Embedding Table Storage and Lookup
Organization Name
Inventor(s)
Gaurav Menghani of Santa Clara CA (US)
Efficient Embedding Table Storage and Lookup - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211458 titled 'Efficient Embedding Table Storage and Lookup
The present disclosure introduces systems, methods, and computer program products for efficient embedding table storage and lookup in machine-learning models. A computer-implemented method involves obtaining an embedding table with multiple embeddings associated with corresponding indexes, compressing each embedding individually for independent decompression, packing the compressed embeddings with a machine-learning model, receiving an input to locate an embedding, determining a lookup value based on the input, searching indexes of the embedding table, locating the embedding based on the lookup value, and decompressing the located embedding independently.
- Embedding table storage and lookup optimization in machine-learning models
- Individual compression of embeddings for independent decompression
- Efficient packing of compressed embeddings with machine-learning models
- Lookup value determination based on input for locating embeddings
- Independent decompression of located embeddings for efficient retrieval
- Potential Applications:
- Natural language processing - Image recognition - Recommendation systems - Anomaly detection - Sentiment analysis
- Problems Solved:
- Improved efficiency in embedding table storage and lookup - Enhanced performance of machine-learning models - Reduced computational resources required for embedding retrieval
- Benefits:
- Faster embedding retrieval - Lower memory usage - Increased accuracy in machine-learning tasks
- Commercial Applications:
"Efficient Embedding Table Storage and Lookup in Machine-Learning Models: Revolutionizing Data Processing in AI Applications"
This technology can be applied in various industries such as e-commerce, healthcare, finance, and cybersecurity for optimizing data processing and enhancing machine-learning model performance.
- Questions about Efficient Embedding Table Storage and Lookup in Machine-Learning Models:
1. How does individual compression of embeddings improve the efficiency of storage and retrieval in machine-learning models?
- Individual compression allows each embedding to be decompressed independently, reducing the overall computational load and improving retrieval speed.
2. What are the potential drawbacks of compressing embeddings individually in terms of performance and accuracy?
- While individual compression may enhance efficiency, it could potentially lead to a loss of accuracy in certain machine-learning tasks that require complex embedding interactions.
Original Abstract Submitted
the present disclosure provides systems, methods, and computer program products for providing efficient embedding table storage and lookup in machine-learning models. a computer-implemented method may include obtaining an embedding table comprising a plurality of embeddings respectively associated with a corresponding index of the embedding table, compressing each particular embedding of the embedding table individually allowing each respective embedding of the embedding table to be decompressed independent of any other embedding in the embedding table, packing the embedding table comprising individually compressed embeddings with a machine-learning model, receiving an input to use for locating an embedding in the embedding table, determining a lookup value based on the input to search indexes of the embedding table, locating the embedding based on searching the indexes of the embedding table for the determined lookup value, and decompressing the located embedding independent of any other embedding in the embedding table.
