Floating Point Processor Prototype Multi-Channel Data Tensor Decomposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Ultra-large data centers face challenges in processing high-capacity, diverse data types efficiently due to high energy consumption and complexity, particularly in handling structured, semi-structured, and unstructured data, which traditional methods struggle to manage effectively.
Innovation Solution
A floating point processor prototype is developed by arranging data into a three-way array, decomposing it into a second-order tensor matrix pattern using higher-order singular value decomposition, and converting it into a sparse domain for block floating point quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used in ultra-large data centers, then data processing capability is maintained at conventional levels, but energy consumption is high and processing efficiency is low
Solution Approach 1:
The patent transforms multi-channel data from conventional 2D matrix representation to 3D tensor representation, fundamentally changing the data structure parameters. This enables more efficient storage and processing operations that reduce energy consumption while increasing processing capability through tensor-specific algorithms
Solution Approach 2:
The patent introduces a third dimension to data organization by representing data as 3D tensors instead of 2D matrices. This dimensional expansion enables parallel processing across multiple channels simultaneously, dramatically improving productivity while the tensor format optimizes memory access patterns to reduce energy consumption
2Productivity
If traditional 2D matrix data structures are used, then data processing is straightforward, but processing efficiency for multi-channel data is insufficient
Solution Approach 1:
The patent transitions from 2D matrix to 3D tensor data structures, adding a third dimension to represent multi-channel data. This dimensional change enables simultaneous processing of multiple data channels, significantly improving processing efficiency despite the increased structural complexity
Solution Approach 2:
The 3D tensor data structure serves multiple functions: it represents multi-channel data, enables parallel processing operations, optimizes memory storage, and facilitates efficient data transformation. This multi-functional approach improves processing efficiency across various operations while managing complexity through a unified data model
3Adaptability or versatility
If diverse data types (structured, semi-structured, unstructured) are processed using conventional methods, then all data types can be handled, but processing complexity and energy consumption increase
Solution Approach 1:
The patent creates a universal 3D tensor data structure that can represent and process all three data types (structured, semi-structured, unstructured) through a single unified framework. This eliminates the need for separate processing pipelines for each data type, reducing processing complexity while maintaining versatility through appropriate tensor transformations and operations
Solution Approach 2:
The patent transforms diverse data types into a common 3D tensor representation, changing their structural parameters to a unified format. This parameter transformation enables consistent processing methods across different data types, reducing complexity while preserving adaptability through the flexibility of tensor operations
Data Source
AI summary
The present invention discloses a floating point processor prototype of multi-channel data. An architecture comprises the following steps: arranging structural data, semi-structured data and unstructured data into a three-way array; decomposing the three-way array into a matrix pattern of a second-order tensor by using higher-order singular value decomposition; and converting the matrix pattern into a sparse domain to conduct block floating point quantization. A floating point processor prototype of multi-channel data is built.