Tensor Address Computation Using Register-Based Multi-Dimensional Indexing
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing multi-dimensional tensors due to the significant computational overhead required for handling nested loop structures, particularly in determining memory addresses, which degrades processor performance.
Innovation Solution
The use of registers to maintain architectural status sets, including loop bound values, current loop index variables, dimension multipliers, and program counter values, allows for efficient computation of memory addresses within nested loops, reducing the need for extensive branch instructions and integer arithmetic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional nested loop structures are used to access multi-dimensional tensors, then the code is easy to write and understand, but the computational overhead is significant due to extensive branch instructions and integer arithmetic operations
Solution Approach 1:
The patent replaces the mechanical execution of nested loop control structures (branch instructions, integer arithmetic operations for index management) with a hardware-based tensor index register system. The processor includes specialized tensor index registers that automatically manage multi-dimensional indexing without requiring software-level nested loops, thereby eliminating the computational overhead of traditional loop control while preserving the ability to access multi-dimensional tensor data structures.
2Adaptability or versatility
If extensive branch instructions and integer arithmetic operations are used to compute memory addresses in nested loops, then general-purpose processors can handle various control flows, but the number of computational cycles increases significantly
Solution Approach 1:
The patent segments the address computation function into dedicated hardware components: tensor index registers for storing indices along different dimensions, dimension size registers for storing the size of each dimension, and a separate address computation unit. This segmentation allows the system to compute tensor element addresses efficiently without requiring general-purpose branch instructions and integer arithmetic, thereby reducing computational cycles while maintaining the ability to handle various tensor access patterns.
3Adaptability or versatility
If traditional memory address computation methods are used, then compatibility with existing processors is maintained, but processor bandwidth is limited due to sequential processing requirements
Solution Approach 1:
The patent implements a nested register structure where tensor index registers are organized hierarchically to represent different dimensions of a tensor. Each level of nesting corresponds to a dimension, with registers for outer dimensions containing indices that are multiplied by their respective dimension sizes and added to the base address, while inner dimension registers provide finer-grained indexing. This nested structure enables efficient computation of addresses for multi-dimensional tensors while maintaining compatibility with existing processor architectures.
Data Source
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AI summary
Methods, systems, and apparatus, including an apparatus for processing an instruction for accessing a N-dimensional tensor, the apparatus including multiple tensor index elements and multiple dimension multiplier elements, where each of the dimension multiplier elements has a corresponding tensor index element. The apparatus includes one or more processors configured to obtain an instruction to access a particular element of a N-dimensional tensor, where the N-dimensional tensor has multiple elements arranged across each of the N dimensions, and where N is an integer that is equal to or greater than one; determine, using one or more tensor index elements of the multiple tensor index elements and one or more dimension multiplier elements of the multiple dimension multiplier elements, an address of the particular element; and output data indicating the determined address for accessing the particular element of the N-dimensional tensor.