Acyclic AI Processor Node Scheduling for Low-Latency Collective Operations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional processors face inefficiencies in handling the complex algorithms and data-intensive workflows of artificial intelligence tasks due to their general-purpose architectures, failing to meet the demands of AI processing for optimized performance and efficiency.
Innovation Solution
An AI-accelerating processor system with bi-directional links and an acyclic subset of hardware processing nodes, each following distinct scheduling patterns, performs computations in a collective operation to generate results through bi-directional link transmissions, optimizing matrix multiplications and other machine learning operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional general-purpose processors are used for AI tasks, then versatility is maintained, but computational efficiency and performance are insufficient
Solution Approach 1:
The processor is divided into multiple specialized hardware processing nodes organized in an acyclic graph structure, where each node can perform specific AI computations. This segmentation allows parallel processing of AI workloads while maintaining manageable complexity through modular design.
Solution Approach 2:
Different hardware processing nodes are assigned different scheduling patterns (first scheduling pattern vs. second scheduling pattern) based on their position and function in the acyclic graph. This local differentiation optimizes computational efficiency for specific AI operations while maintaining overall system versatility.
2Productivity
If hardware processing nodes use the same scheduling pattern, then coordination is simplified, but computational efficiency for AI operations is reduced
Solution Approach 1:
The system implements different scheduling patterns for different hardware processing nodes based on their roles in the acyclic graph. First hardware processing nodes use a first scheduling pattern while second hardware processing nodes use a second scheduling pattern, optimizing computational efficiency for diverse AI operations.
Solution Approach 2:
The scheduling patterns are dynamically assigned based on the operational context and node position in the acyclic graph, allowing the system to adapt scheduling behavior to match the specific computational requirements of different AI workloads and graph configurations.
3Speed
If processing nodes are densely connected, then data transmission speed is improved, but latency increases due to more transmission paths
Solution Approach 1:
The system uses an acyclic graph structure with asymmetric connections where end nodes are strategically disconnected from other end nodes. This asymmetric design reduces unnecessary transmission paths and latency while maintaining efficient data flow through the processing nodes.
Solution Approach 2:
The hardware processing nodes are segmented into end nodes and intermediate nodes with different connection characteristics. This segmentation allows optimization of data transmission paths, ensuring that not all nodes are densely connected, thereby reducing latency while maintaining necessary communication efficiency.
Data Source
AI summary
An AI-accelerating processor system may include an acyclic subset of hardware processing nodes. The acyclic subset includes a plurality of end nodes that are disconnected from other end nodes in the acyclic subset. The acyclic subset of hardware processing nodes is configured to perform, according to schedules, computations that are part of a collective operation. A first hardware processing node in the subset has a first scheduling pattern and a second hardware processing node in the subset has a second scheduling pattern that is different from the first scheduling pattern to account for the subset being acyclic. The acyclic subset of hardware processing nodes is also configured to transmit computation outputs to neighboring hardware processing nodes among the acyclic subset through the bi-directional links to generate a result that is part of the collective operation. The result is contributed by each of the hardware processing nodes in the acyclic subset.