Matrix Processor Circuit Data Reshape and Transport
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital processing systems, particularly those using the Von Neumann architecture, face inefficiencies in performing complex calculations due to the sequential nature of data processing, which leads to high time consumption and energy expenditure. Additionally, artificial intelligence applications, which heavily rely on matrix operations, require optimized computational methods to manage data dependencies and maximize processing efficiency.
Innovation Solution
The development of specialized Matrix Processor circuits and arrays, designed to efficiently perform matrix operations by optimizing data loading, processing, and output, while also implementing innovative control systems and memory architectures to minimize data movement and latency. This includes the use of wide and deep memory systems, pipelined processing, and reflexive array architectures to handle forward and backward propagation operations in artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If specialized matrix processor circuits are used to perform matrix operations, then computational speed and efficiency are improved, but device complexity increases
Solution Approach 1:
The system divides the matrix processing task into multiple specialized matrix processor circuits arranged in an array, where each processor handles a specific portion of the matrix operation. This segmentation allows parallel processing of matrix elements, significantly improving computational speed while distributing the overall complexity across multiple simpler processing units rather than requiring one complex processor.
Solution Approach 2:
The matrix processor circuits are designed with universal functionality to perform various matrix operations including multiplication, accumulation, and data transformation. Each processor can handle different matrix dimensions and operation types, reducing the need for specialized hardware for each specific task and thereby managing device complexity while maintaining high productivity.
2Adaptability or versatility
If data is moved frequently between memory and processing units in Von Neumann architecture, then processing flexibility is maintained, but time consumption and energy expenditure increase
Solution Approach 1:
The system implements a nested memory architecture where multiple levels of memory are integrated within and around the matrix processor array. Local memory buffers are embedded within each processor, while deeper memory layers are positioned adjacent to the processor array. This nesting reduces the average distance data must travel, minimizing transfer time and energy while maintaining the flexibility to access different memory depths as needed.
Solution Approach 2:
The patent introduces intermediary buffer memory structures that sit between the main memory system and the matrix processor circuits. These buffers act as intermediaries, pre-loading data into proximity of the processors before actual computation begins. This intermediary layer reduces the frequency and distance of data transfers during processing, thereby reducing time consumption and energy expenditure while preserving processing flexibility.
3Productivity
If multiple matrix processor circuits are used to handle complex AI tasks, then computational throughput is improved, but coordination overhead and idle time increase
Solution Approach 1:
The control system implements continuous scheduling algorithms that ensure all matrix processor circuits remain actively engaged in useful computation throughout the processing cycle. By carefully orchestrating data flow and task distribution, the system eliminates idle periods between operations, maintaining continuous productive action across the entire processor array and maximizing computational throughput without excessive coordination overhead.
Solution Approach 2:
The system incorporates feedback mechanisms where the control system continuously monitors the status and workload of each matrix processor circuit. Based on this feedback, the control system dynamically adjusts task allocation and data flow routing to balance the workload across processors. This feedback-driven coordination ensures that no processor remains idle while others are overloaded, optimizing throughput and minimizing coordination overhead.
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 great 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, controlling, and reshaping data in Matrix Processor circuits efficiently.