Looping Instruction for Sparse Matrix Multiplication Efficiency
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Solution Overview
Problem
Existing processors inefficiently handle matrix multiplications involving sparse matrices, leading to wasted time, memory bandwidth, and power due to numerous zero elements in the matrices.
Innovation Solution
A processor with a looping instruction, specifically the Vector-Load-Sparse-Multiply-Accumulate (VLSMA) instruction, efficiently executes sparse matrix operations by using a Sparse Matrix Compiled Object (SMCO) format, where non-zero values and index tuples are stored, allowing parallel execution and optimized resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processors execute matrix multiplications with sparse matrices using standard instruction sets, then the operations can be performed, but computational overhead increases and efficiency decreases due to numerous zero elements
Solution Approach 1:
The patent extracts and processes only the non-zero elements of sparse matrices by using a specialized looping instruction that operates on compressed sparse row (CSR) format data structures. This extraction principle eliminates the need to process zero elements, directly improving computational efficiency and reducing overhead time for matrix multiplications involving sparse matrices.
Solution Approach 2:
The patent applies local quality by creating a specialized instruction (looping instruction with opcode 0x1A) that is specifically optimized for sparse matrix operations rather than general-purpose matrix multiplication. This localized optimization allows the processor to handle sparse matrix data structures with appropriate granularity, improving productivity while minimizing computational overhead.
2Use of energy by moving object
If processors process all elements in dense matrix formats, then operations are simplified, but memory bandwidth is wasted and power consumption increases due to processing zero elements
Solution Approach 1:
The patent extracts only the necessary non-zero elements from sparse matrices and processes them using specialized looping instructions. This extraction approach prevents the processor from consuming energy and using memory bandwidth to fetch and process zero elements, directly addressing the energy efficiency and memory bandwidth waste problems associated with dense matrix processing of sparse data.
Solution Approach 2:
The patent applies partial action by performing matrix multiplication operations only on the non-zero elements of sparse matrices rather than all elements. The looping instruction processes a variable number of iterations based on the actual count of non-zero elements, avoiding excessive processing of zero elements and thereby reducing power consumption and memory bandwidth usage.
3Device complexity
If processors use standard looping constructs for sparse matrix operations, then the implementation is simple, but the number of instructions increases and execution time is extended
Solution Approach 1:
The patent merges multiple operations into a single specialized looping instruction (opcode 0x1A) that combines loop control, memory access, and arithmetic operations. This merging consolidates what would otherwise require multiple separate instructions into one unified operation, reducing the total instruction count and improving execution speed while maintaining manageable instruction set complexity through systematic design.
Solution Approach 2:
The looping instruction is designed as a universal multi-functional operation that can handle various sparse matrix operations including multiplication, accumulation, and transformation. By creating a single instruction that performs multiple functions relevant to sparse matrix processing, the patent improves productivity without significantly increasing overall instruction set complexity, as the same instruction structure handles different operations.
Data Source
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AI summary
A processor (101) with an instruction set comprising a looping instruction and corresponding method are provided. The looping instruction is defined by a corresponding opcode and comprises a register operand for holding a sequence of subinstructions. The looping instruction causes the processor (101) to: repeatedly execute the looping instruction without incrementing a program counter, each execution of the looping instruction comprising: executing one of the subinstructions in the register operand; and modifying the register operand to contain a different set of subinstructions.