Dynamic Data Conversion for Sparse Formats in Compute Networks
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Solution Overview
Problem
Static sparse representation and precision formats in collective computing systems limit the types of computing nodes and switch circuitries that can be used, restricting network bandwidth and flexibility.
Innovation Solution
Incorporating conversion circuitry in computing nodes and switch circuitries to dynamically convert sparse representations and precision formats of data signals, allowing for the use of unsupported formats and increasing design flexibility and network throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static sparse representation and precision format is used to ensure compatibility across all computing nodes and switch circuitries, then system reliability is improved, but adaptability and network bandwidth are limited
Solution Approach 1:
The patent implements dynamic format conversion by introducing conversion circuitry that can adaptively transform data between different sparse representations and precision formats. This allows the system to dynamically select optimal formats for different computing nodes and switch circuitries, resolving the contradiction between maintaining compatibility and enabling format flexibility.
Solution Approach 2:
The conversion circuitry acts as an intermediary component between the processor and the network infrastructure. It translates data signals into formats suitable for transmission across the network, enabling compatibility between diverse computing nodes and switch circuitries while maintaining the ability to utilize specialized formats at each endpoint.
2Device complexity
If a static sparse representation and precision format is used, then system complexity is reduced, but network bandwidth and design flexibility are restricted
Solution Approach 1:
The patent segments the data transmission function into two parts: the processor generates data in its native format, and the conversion circuitry handles format transformation separately. This segmentation allows each component to operate independently at optimal performance levels, increasing network bandwidth without significantly increasing overall system complexity.
Solution Approach 2:
The conversion circuitry changes parameters such as precision level and sparsity representation dynamically based on the requirements of the receiving component. This enables the system to optimize network bandwidth by selecting appropriate parameter combinations for different transmission scenarios without requiring complete system redesign.
3Productivity
If diverse computing nodes and switch circuitries are utilized to increase network throughput, then productivity is improved, but compatibility and system reliability are compromised
Solution Approach 1:
The conversion circuitry provides universal interface functionality that enables diverse computing nodes and switch circuitries to communicate effectively. By implementing multiple conversion capabilities within a single component, the system achieves multi-functionality that maintains compatibility across heterogeneous hardware while enabling high-throughput performance.
Data Source
AI summary
A computing node for a computing system includes a processor, conversion circuitry, and routing circuitry. The processor generates a data signal based on a function of an application executed by the computing system. The data signal has a first precision format and a first sparse representation. The conversion circuitry receives the data signal from the processor and generate a converted data signal by at least one of converting the first precision format to a second precision format and converting the first sparse representation to a second sparse representation. The routing circuitry transmits the converted data signal to switch circuitry of the computing system.