Matrix Processor Array Control for AI Parallel Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional Von Neumann computer architecture is inefficient for complex calculations due to sequential processing iterations, leading to high time and energy consumption, particularly in computationally intensive fields like Artificial Intelligence that heavily rely on matrix operations.
Innovation Solution
The development of specialized Matrix Processor circuits with a wide SRAM memory system and control system to perform matrix operations efficiently, allowing for parallel processing and coordination of multiple Matrix Processors in arrays to optimize AI tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional Von Neumann computer architecture is used for complex calculations, then the system can perform any desired calculation through sequential processing steps, but the processing time becomes extremely long due to repeated data movement between memory and ALU
Solution Approach 1:
The patent divides the complex calculation task into multiple matrix operation sub-tasks that can be executed in parallel. The matrix processor array segments the computational workload across multiple independent processing units, each capable of performing matrix operations simultaneously, thereby reducing overall processing time while maintaining calculation versatility.
Solution Approach 2:
The patent transitions from sequential one-dimensional processing in Von Neumann architecture to two-dimensional parallel processing using a matrix processor array. By organizing processors in a grid structure with row and column interconnects, the system enables simultaneous execution of multiple matrix operations, effectively adding a spatial dimension to the processing paradigm.
2Speed
If specialized Matrix Processor circuits are used to perform matrix operations, then processing speed for matrix computations is significantly improved, but coordination between multiple Matrix Processors becomes complex
Solution Approach 1:
The patent implements a universal control system that can manage multiple matrix processors through standardized interfaces and protocols. The control system provides multi-functional capabilities including data routing, operation scheduling, and result aggregation, allowing it to coordinate diverse matrix processing tasks without requiring processor-specific control logic.
Solution Approach 2:
The patent introduces intermediate buffer structures and control logic that mediate between multiple matrix processors and the external system. These intermediaries handle data transfer, synchronization, and coordination tasks, isolating the complexity of multi-processor management from both the processors themselves and the external control interface.
3Productivity
If multiple Matrix Processors are coordinated to perform parallel processing, then computational efficiency is greatly enhanced, but data loading and reloading time increases due to memory access requirements
Solution Approach 1:
The patent merges the memory systems of multiple matrix processors into a shared or interconnected memory architecture. This allows data to be loaded once and made available to multiple processors simultaneously, or enables efficient data sharing between processors without repeated loading from external memory, thereby reducing overall data loading time while maintaining high computational throughput.
Data Source
AI summary
Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is extremely computationally intensive field such that it can be expensive, time consuming, and energy consuming. Fortunately, many of the calculations required for artificial intelligence can be performed in parallel such that specialized processors can greatly increase computational performance. Specifically, artificial intelligence generally requires large numbers of matrix operations to implement neural networks such that specialized Matrix Processor circuits can improve performance. But a neural network is more than a collection of matrix operations; it is a set of specifically coordinated matrix operations with complex data dependencies. Without proper coordination, Matrix Processor circuits may end up idle or spending large amounts of time loading in different weight matrix data. Thus this document discloses apparatus and methods for organizing and controlling multiple Matrix Processor circuits efficiently.