Processor Datapath for Matrix Multiply Accumulate Operations
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Solution Overview
Problem
Conventional processors are inefficient in performing matrix multiply and accumulate operations due to the need to break down these operations into series of basic arithmetic operations on scalar operands, leading to inefficient use of register file bandwidth and datapath resources.
Innovation Solution
A processor datapath is designed to execute matrix multiply and accumulate operations by loading multiple vectors into operand collectors and performing dot product operations in parallel, reducing the need to reload operands from the register file and optimizing data reuse within the datapath.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If matrix operations are broken down into series of basic arithmetic operations on scalar operands, then processors can use existing arithmetic logic units to perform calculations, but register file bandwidth is inefficiently used and processing speed is reduced
Solution Approach 1:
The patent loads multiple vector operands into the datapath in advance before executing the matrix multiply operations. By pre-loading the necessary data into operand collectors and holding it in the datapath during execution, the system eliminates repeated reload operations from the register file, thereby reducing time loss and improving processing speed.
Solution Approach 2:
The patent maintains continuous data flow through the datapath by keeping loaded operands available for multiple sequential dot product operations. Instead of reloading data for each operation, the system continuously processes using the same loaded data, maximizing the useful action time and eliminating idle reload cycles.
2Productivity
If matrix operations are broken down into basic arithmetic operations, then existing processor hardware can be used, but datapath resources are not optimized for matrix operations
Solution Approach 1:
The patent designs the datapath to serve multiple functions: it handles both scalar arithmetic operations and matrix operations through the same core infrastructure. By making the datapath universal and capable of accommodating different data types and operation modes, the system avoids the need for separate specialized hardware for matrix operations, thereby managing complexity while improving efficiency.
Solution Approach 2:
The patent implements a dynamic datapath configuration that can adapt its behavior based on the operation type. The datapath can dynamically switch between scalar and matrix operation modes, loading appropriate number of operands and configuring execution units accordingly. This dynamic adaptability allows the same hardware to optimize for different workloads without requiring complex dedicated circuits for each case.
3Productivity
If scalar arithmetic operations are executed sequentially, then processor logic is simple to implement, but processing time increases significantly for large matrices
Solution Approach 1:
The patent segments the matrix multiplication task into multiple independent dot product operations that can be executed in parallel. By dividing the large matrix operation into smaller units (dot products of vectors), the system enables concurrent execution of multiple operations, thereby reducing total processing time while maintaining simple sequential logic within each segment.
Solution Approach 2:
The patent transitions from one-dimensional sequential scalar operations to multi-dimensional parallel vector operations. By organizing operations in a dimensional hierarchy where multiple dot products can proceed simultaneously across different dimensions, the system achieves parallelism and significantly reduces execution time for large matrix multiplications.
Data Source
AI summary
A method, computer readable medium, and processor are disclosed for performing matrix multiply and accumulate (MMA) operations. The processor includes a datapath configured to execute the MMA operation to generate a plurality of elements of a result matrix at an output of the datapath. Each element of the result matrix is generated by calculating at least one dot product of corresponding pairs of vectors associated with matrix operands specified in an instruction for the MMA operation. A dot product operation includes the steps of: generating a plurality of partial products by multiplying each element of a first vector with a corresponding element of a second vector; aligning the plurality of partial products based on the exponents associated with each element of the first vector and each element of the second vector; and accumulating the plurality of aligned partial products into a result queue utilizing at least one adder.