Processor Cluster Address Generation for Tensor Flattening
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Solution Overview
Problem
Traditional processors and processing techniques are inadequate for handling the immense computational requirements of vast quantities of unstructured data, such as those encountered in big data applications.
Innovation Solution
The use of processor cluster address generation techniques, which involve accessing processor clusters capable of executing software-initiated work requests, flattening tensors into single dimensions, parsing work request address fields, and generating addresses for direct memory access (DMA) operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processors are used to handle data processing, then device complexity is low, but productivity is insufficient for vast quantities of unstructured data
Solution Approach 1:
The system segments data processing into multiple dimensions by flattening multi-dimensional tensors into single-dimensional addressable memory spaces. This allows traditional processors to efficiently handle high-dimensional data through linear address generation, resolving the contradiction between maintaining simple processor architecture and achieving high productivity for unstructured data processing
Solution Approach 2:
The patent transforms multi-dimensional data structures into single-dimensional address spaces through flattening operations. By converting N-dimensional tensor coordinates into linear memory addresses using address generation circuits, the system enables efficient processing of vast quantities of unstructured data without requiring complex multi-dimensional processor architectures
2Adaptability or versatility
If multi-dimensional memory access is implemented, then data manipulation capability is improved, but device complexity increases
Solution Approach 1:
The patent introduces address generation circuits as intermediary components between the processor and memory system. These circuits automatically perform the complex task of converting multi-dimensional tensor coordinates into linear memory addresses, enabling versatile data manipulation capabilities while shielding the processor from the underlying complexity of multi-dimensional memory access requirements
Data Source
AI summary
Techniques for data manipulation using processor cluster address generation are disclosed. One or more processor clusters capable of executing software-initiated work requests are accessed. A plurality of dimensions from a tensor is flattened into a single dimension. A work request address field is parsed, where the address field contains unique address space descriptors for each of the plurality of dimensions, along with a common address space descriptor. A direct memory access (DMA) engine coupled to the one or more processor clusters is configured. Addresses are generated based on the unique address space descriptors and the common address space descriptor. The plurality of dimensions can be summed to generate a single address. Memory is accessed using two or more of the addresses that were generated. The addresses are used to enable DMA access.