Matrix Computing Device Pipeline Architecture
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Solution Overview
Problem
General-purpose processors are inefficient in processing information, especially with large loads, leading to low processing efficiency and long computation times for matrix operations in machine learning models.
Innovation Solution
A computing device with an operation unit, controller unit, and storage unit is designed to parse calculation instructions, extract operation codes and data, and perform matrix calculations efficiently, utilizing a structure of n-stage pipeline to improve processing speed and efficiency, and can be interconnected via a fast PCIE bus for larger-scale operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a general-purpose processor is used to process information with large load, then the processor can handle various types of computations, but the processing efficiency is low and computation time is long
Solution Approach 1:
The processor is divided into multiple computing units (first computing unit, second computing unit, etc.) that can independently perform different operations simultaneously. Each computing unit handles specific computational tasks in parallel, breaking down the overall computation into smaller segments that can be processed concurrently, thereby improving processing efficiency and reducing computation time.
Solution Approach 2:
Multiple computing units are merged into a single processor system that can execute multiple operations simultaneously. The processor combines vector computing units, matrix computing units, and other specialized units to handle complex computations in parallel, achieving higher throughput and faster computation times compared to sequential processing.
2Adaptability or versatility
If a general-purpose processor runs a software program to process information, then the processor is versatile, but the operating speed is limited and efficiency is low
Solution Approach 1:
The processor dynamically switches between different computing modes and configurations based on the specific computational task. Computing units can be activated or deactivated as needed, and the processor can adapt its internal data flow and operation sequences to match the requirements of different algorithms, maintaining versatility while optimizing operating speed for each specific task.
Solution Approach 2:
The processor changes operational parameters such as data precision, computation mode, and resource allocation based on the specific computational requirements. By adjusting these parameters dynamically, the processor can optimize its operating speed for different types of computations while maintaining the ability to handle various computational tasks.
Data Source
AI summary
A computing device and related products are provided. The computing device is configured to perform machine learning calculations. The computing device includes an operation unit, a controller unit, and a storage unit. The storage unit includes a data input/output (I/O) unit, a register, and a cache. Technical solution provided by the present disclosure has advantages of fast calculation speed and energy saving.