Acyclic AI Processor Architecture for Low-Latency Collective Operations
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Solution Overview
Problem
Conventional processors face inefficiencies in handling the complex algorithms and data-intensive workflows of artificial intelligence tasks due to their architecture and instruction sets optimized for traditional computing, failing to meet the demands of AI processing efficiently.
Innovation Solution
An AI-accelerating processor system with bi-directional links and an acyclic subset of hardware processing nodes, where each node follows unique scheduling patterns to perform computations and transmit results through bi-directional links, optimizing collective operations like matrix multiplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processors are used for AI tasks, then general-purpose computation capability is maintained, but computational efficiency and performance for AI algorithms deteriorate
Solution Approach 1:
The processor is divided into multiple specialized processing cores, each capable of performing specific AI operations. The computation is segmented into collective operations distributed across these cores, with each core handling a portion of the overall computation while maintaining coordination through the acyclic communication pattern.
Solution Approach 2:
The processor employs dynamic scheduling patterns where different processing nodes operate according to different schedules within the acyclic structure. This allows the system to adapt computation timing and data flow dynamically while maintaining the acyclic dependency relationships, optimizing performance for different AI workloads.
2Productivity
If traditional processor architecture is used, then simplicity of design is maintained, but ability to handle data-intensive workflows deteriorates
Solution Approach 1:
The processing nodes in the acyclic architecture are designed to perform multiple functions including computation, data transmission, and coordination with neighboring nodes. Each node can participate in different collective operations and adapt its behavior based on the scheduling pattern, providing universal functionality for various AI workloads.
Solution Approach 2:
The acyclic communication structure acts as an intermediary framework that coordinates data flow between processing nodes. The bi-directional links and scheduling mechanisms serve as mediators that enable efficient data-intensive workflows while maintaining architectural organization and preventing deadlocks.
3Speed
If cyclic communication patterns are used among processing nodes, then full connectivity is achieved, but latency and computational overhead increase
Solution Approach 1:
The patent extracts the cyclic communication pattern from the architecture and replaces it with an acyclic structure. By removing the cyclic dependencies that cause latency and overhead, the system achieves faster computation speeds while maintaining necessary connectivity through the acyclic graph structure with dedicated communication paths.
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.