Vector Computational Unit ISA for Low-Overhead AI Parallelism
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Solution Overview
Problem
Existing computer processors, including GPUs and traditional CPUs, are inefficient for performing machine learning and artificial intelligence operations on large datasets in parallel without the overhead of multiple processing cores.
Innovation Solution
A microprocessor system with a vector computational unit and computational array that performs arithmetic operations on input vectors in parallel using a plurality of computation units, each processing the same computation in response to a single instruction, and a control unit to coordinate data flow and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional computer processors are used to perform machine learning operations, then single mathematical operation speed is maintained, but parallel processing capability on large datasets is insufficient
Solution Approach 1:
The processor is segmented into multiple processing cores, each capable of independent execution. This allows the system to divide large datasets into smaller chunks and process them in parallel across multiple cores, significantly improving parallel processing capability while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent transitions from single-core sequential processing to multi-core parallel processing, adding a temporal dimension to data processing. Multiple operations can now execute simultaneously across different cores, effectively moving from one-dimensional sequential execution to multi-dimensional parallel execution space
2Productivity
If GPUs are used to perform parallel operations on large datasets, then parallel processing speed is improved, but overhead from multiple processing cores increases
Solution Approach 1:
Each processing core is designed with universal functionality to execute multiple types of operations including matrix multiplications, convolutions, and element-wise operations. This multi-functionality reduces the need for specialized hardware for each operation type, thereby reducing overall system overhead while maintaining high parallel processing speed
Solution Approach 2:
The processor supports dynamic configuration of operational parameters such as precision (32-bit, 16-bit, 8-bit), data types, and operation modes. This flexibility allows the system to optimize performance for specific machine learning workloads without requiring dedicated hardware for each parameter configuration, reducing overhead while maintaining productivity
Data Source
AI summary
A microprocessor system comprises a vector computational unit and a control unit. The vector computational unit includes a plurality of processing elements. The control unit is configured to provide at least a single processor instruction to the vector computational unit. The single processor instruction specifies a plurality of component instructions to be executed by the vector computational unit in response to the single processor instruction and each of the plurality of processing elements of the vector computational unit is configured to process different data elements in parallel with other processing elements in response to the single processor instruction.