Looping Instruction Register for Sparse Matrix Multiplication
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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 multiplications by encoding matrices as Sparse Matrix Compiled Objects (SMCOs) and using a register operand to modify subinstructions, allowing parallel execution of index tuples by multipliers and accumulators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing processors execute matrix multiplications using standard instruction sets, then general-purpose computation is achieved, but computational efficiency deteriorates due to wasted operations on zero elements in sparse matrices
Solution Approach 1:
The patent segments the matrix multiplication process into distinct phases: loading non-zero elements, executing multiply-accumulate operations, and managing index tuples. By breaking down the computation into these segments, the processor can selectively operate only on relevant data elements rather than processing entire matrices, thereby improving computational efficiency and reducing energy waste on zero elements.
Solution Approach 2:
The patent implements local quality by creating specialized hardware components within the processor: a dedicated vector unit for parallel operations, specific register operands for holding subinstructions and index tuples, and tailored execution pipelines. These localized specialized components optimize the processing of sparse matrix operations without requiring complete redesign of the entire processor architecture.
2Productivity
If standard instruction sets are used for sparse matrix operations, then instruction simplicity is maintained, but execution time increases due to inability to efficiently handle sparse data structures
Solution Approach 1:
The patent applies preliminary action by pre-loading non-zero matrix elements and their corresponding index tuples into dedicated registers before execution. The index tuples, which contain row indices, column indices, and non-zero value indices, are prepared in advance and stored in the vector unit's registers. This preliminary preparation eliminates the need for runtime searching and conditional checks, significantly reducing execution time.
Solution Approach 2:
The patent introduces dynamic subinstructions that can be loaded into a register operand and executed sequentially. These subinstructions are dynamically loaded from memory into the vector unit, allowing the processor to adapt its execution sequence based on the specific sparse matrix structure. This dynamic approach enables efficient handling of varying sparsity patterns without requiring static instruction sequences.
3Productivity
If processors process all matrix elements uniformly, then implementation simplicity is maintained, but resource utilization deteriorates due to processing zero elements unnecessarily
Solution Approach 1:
The patent extracts only the non-zero elements from the sparse matrix and processes them, leaving zero elements untouched. The index tuples explicitly identify which elements are non-zero, allowing the vector unit to extract and process only the relevant data. This extraction principle eliminates unnecessary memory accesses and computational operations on zero elements, improving resource utilization and reducing energy consumption.
Solution Approach 2:
The patent changes the operational parameters by using specialized register operands to hold subinstructions and index tuples, and by configuring the vector unit to operate in parallel modes. These parameter changes enable the processor to switch from uniform sequential processing to selective parallel processing, optimizing resource utilization for sparse matrix operations.
Data Source
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.