Vector Compute Unit Parallel Array for ML Data Throughput
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Solution Overview
Problem
Existing computer processors, including GPUs and CPUs, are inefficient for performing machine learning and artificial intelligence operations on large datasets due to their lack of parallel processing capabilities tailored for these specific tasks, leading to overhead and suboptimal performance.
Innovation Solution
A microprocessor system incorporating a vector computational unit and computational array that performs arithmetic operations on input vectors in parallel, utilizing a control unit to coordinate instructions across multiple processing elements, enabling simultaneous processing of large datasets with reduced overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional computer processors or GPUs are used to perform machine learning operations, then mathematical operations can be performed, but processing efficiency and performance are insufficient due to lack of parallel processing capabilities tailored for these specific tasks
Solution Approach 1:
The processor is divided into multiple processing elements (PEs) that can operate in parallel. Each PE is a simplified unit capable of performing basic arithmetic operations, and multiple PEs work simultaneously on different elements of large datasets, enabling efficient parallel processing for machine learning workloads without requiring complex multi-core coordination
Solution Approach 2:
The processing elements are designed with universal functionality to perform various arithmetic operations (addition, multiplication, accumulation) required by different machine learning algorithms. The same PE architecture handles different operations across different layers of neural networks, eliminating the need for specialized hardware for each operation type
2Quantity of substance
If multiple processor cores are used to perform parallel operations, then larger datasets can be processed, but computational overhead increases
Solution Approach 1:
The data is segmented into vectors that are distributed across multiple processing elements. Each PE processes a portion of the vector in parallel, and the segmentation enables efficient utilization of hardware resources without requiring complex inter-core communication and coordination overhead
Solution Approach 2:
Multiple simple processing elements are merged into a unified parallel processing array that operates under single instruction control. This combining approach achieves parallel processing capability without the overhead of multiple independent processor cores, as the PEs share control logic and can be coordinated efficiently
3Adaptability or versatility
If traditional processors are used for machine learning tasks, then general-purpose computing can be performed, but performance on specific machine learning operations is suboptimal
Solution Approach 1:
The processing elements can dynamically change their operational parameters to accommodate different machine learning operations. By adjusting parameters such as data types, precision levels, and operation modes, the same hardware architecture efficiently handles various machine learning workloads from convolutional neural networks to recurrent neural networks
Data Source
AI summary
A microprocessor system comprises a computational array and a vector computational unit. The computational array includes a plurality of computation units. The vector computational unit is in communication with the computational array and includes a plurality of processing elements. The processing elements are configured to receive output data elements from the computational array and process in parallel the received output data elements.