Interleaved Data Tensor Transformation via Index Calculation
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Solution Overview
Problem
Conventional machine learning systems face inefficiencies when performing transformation operations on interleaved data, as they often require de-interleaving and re-interleaving, which introduces substantial computational overhead and reduces the benefits of parallel operations.
Innovation Solution
A method that determines a transformation operation for interleaved data tensors by calculating realized dimensions and applying the transformation directly, using memory copies to account for interleaving and padding, thereby eliminating the need for de-interleaving and re-interleaving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is de-interleaved and re-interleaved for transformation operations, then the data can be processed correctly, but computational overhead increases substantially
Solution Approach 1:
The patent extracts and eliminates the unnecessary de-interleaving and re-interleaving steps from the transformation operation pipeline. By directly operating on interleaved data through carefully designed index calculations, the method removes the harmful intermediate conversion steps while preserving the correctness of the transformation operation.
Solution Approach 2:
The patent introduces index calculation logic as an intermediary mechanism that bridges the gap between interleaved data structure and transformation requirements. Instead of physically de-interleaving data, the method uses computed indices to directly access and transform elements in their interleaved positions, maintaining correctness without the overhead of data reorganization.
2Productivity
If data is padded to fill vector registers for parallel operations, then parallelization efficiency improves, but the amount of actual data processed decreases
Solution Approach 1:
The patent applies partial action by processing only the valid data elements within the padded vector register through precise index calculations. The transformation operation selectively accesses only the actual data elements (ignoring padding) while still benefiting from the parallel execution capability of the full vector register, thus maintaining high productivity without wasting resources on padding data.
3Ease of manufacture
If conventional de-interleave and re-interleave operations are performed, then data can be transformed, but resource usage increases
Solution Approach 1:
The patent extracts and eliminates the resource-intensive de-interleave and re-interleave operations from the transformation pipeline. By directly operating on interleaved data through index-based access patterns, the method achieves the same transformability while significantly reducing computational resource usage, including CPU cycles, memory access operations, and energy consumption.
Data Source
AI summary
Method and apparatus for improved machine learning transformation operations on interleaved data are provided. A first interleaved data tensor having an unrealized set of dimensions is received, and a transformation operation to apply to the first interleaved data tensor is determined. A realized set of dimensions for output of the transformation operation is determined based on the unrealized set of dimensions and the transformation operation. A second interleaved data tensor is generated by applying the transformation operation to the first interleaved data tensor, comprising copying input elements in the first interleaved data tensor to output elements in the second interleaved data tensor based on indices in the realized set of dimensions.