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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Hardware-Assisted Low-Latency NPU Virtualization Method for Multi-Sensor AI Systems.

Jong-Hwan Jean¹, Dong-Sun Kim¹

¹Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

This study enhances AI processing by virtualizing neural processing units (NPUs) to run multiple models simultaneously. This improves resource utilization and reduces latency for real-time applications like autonomous driving.

Keywords:

hardware scheduler multi-sensor neural processing unit prefetching virtualization

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Area of Science:

Artificial Intelligence
Computer Engineering
Embedded Systems

Background:

AI systems in autonomous driving and smart homes require processing complex text and image data.
Current multi-sensor systems face challenges with low resource utilization and memory latency.
Integrating NPUs and sensors improves speed but not always efficiency.

Purpose of the Study:

To reduce processing time and enhance resource utilization in AI systems.
To address challenges of low resource utilization and memory latency in NPU-based systems.
To enable efficient multitasking and low-latency processing in resource-constrained environments.

Main Methods:

Virtualizing neural processing units (NPUs) to handle multiple deep-learning models concurrently.
Implementing a hardware scheduler to manage task execution and optimize resource allocation.
Utilizing data prefetching techniques to minimize memory latency.

Main Results:

Hardware scheduler reduced memory cycles by over 10% across tested models.
Significant reductions in memory latency observed, e.g., 30% for NCF and 70% for DLRM.
Minimized NPU idle time and memory latency, particularly in environments with frequent context switching.

Conclusions:

The proposed virtualization method effectively improves NPU resource utilization and reduces processing latency.
This approach is highly beneficial for real-time AI applications demanding efficient multitasking.
Optimized performance in resource-constrained settings is achieved, supporting applications like autonomous driving and smart homes.