Event-Driven NPU Performance Estimation Before Hardware Development
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Solution Overview
Problem
Existing methods for measuring the performance of neural processing units (NPUs) are time-consuming and resource-intensive, requiring extensive simulation and hardware operations, and lack efficient abstraction techniques for performance estimation.
Innovation Solution
A method and device for measuring NPU performance through abstraction, involving hardware modeling, event modeling, and simulation to estimate performance using hardware specifications, allowing for accurate and rapid assessment of NPU capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional performance measurement methods using actual hardware or virtual device simulation are used, then measurement accuracy is improved, but measurement time and resource consumption increase significantly
Solution Approach 1:
The patent creates an event model that copies the essential operational characteristics of the NPU without requiring actual hardware or full virtual simulation. The event model includes nodes representing hardware components and edges representing data flow, allowing performance estimation through simplified simulation that captures key operational patterns while avoiding the time-consuming nature of complete hardware testing or detailed virtual device simulation.
Solution Approach 2:
The patent transforms the performance measurement approach by changing from direct hardware measurement to parameter-based estimation. Hardware specifications are converted into event model parameters that represent operational characteristics, allowing performance to be estimated through mathematical modeling rather than physical measurement, thereby reducing measurement time while maintaining reasonable accuracy.
2Reliability
If extensive hardware operations and virtual device simulation are performed, then performance measurement reliability is improved, but resource consumption and complexity increase
Solution Approach 1:
The patent extracts only the essential operational elements from the complete NPU system to create the event model. Instead of simulating or measuring every aspect of the hardware, the method identifies and models only the critical components (nodes) and their interactions (edges), reducing measurement system complexity while maintaining reliability by focusing on the most impactful operational characteristics.
Solution Approach 2:
The patent segments the NPU performance measurement into discrete events and operations that can be individually modeled and analyzed. The calculation task is divided into multiple events represented in the event model, allowing complex performance characteristics to be broken down into manageable components that can be simulated and measured independently, then aggregated to provide overall performance estimation.
Data Source
AI summary
A method for measuring performance of neural processing devices and devices for measuring performance are provided. The method for measuring performance of neural processing devices comprises receiving hardware information of a neural processing device, modeling hardware components according to the hardware information as agents, dividing a calculation task by events for the agents and modeling the calculation task, thereby generating an event model which includes nodes corresponding to the agents and edges corresponding to the events and measuring a total duration of the calculation task through simulation of the event model.