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This report discusses the importance of optimizing energy consumption for AI inference in edge computing, particularly for IoT applications. It outlines strategies such as model compression, efficient architectures, hardware acceleration, and dynamic voltage scaling to enhance energy efficiency. A case study demonstrates significant power savings in a smart agriculture sensor node, highlighting the potential for multi-day operation on low-power devices.

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22 views1 page

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This report discusses the importance of optimizing energy consumption for AI inference in edge computing, particularly for IoT applications. It outlines strategies such as model compression, efficient architectures, hardware acceleration, and dynamic voltage scaling to enhance energy efficiency. A case study demonstrates significant power savings in a smart agriculture sensor node, highlighting the potential for multi-day operation on low-power devices.

Uploaded by

scribdlog
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Title:

Energy Efficiency in Edge AI Devices

Abstract:
As edge computing becomes increasingly prevalent in Internet of Things (IoT)
applications, optimizing energy consumption for AI inference at the edge is
critical. This report explores recent strategies for improving energy efficiency in
edge AI devices, including model compression, hardware acceleration, and dynamic
voltage scaling.

1. Introduction
Edge AI enables real-time data processing on low-power devices without relying on
constant cloud connectivity. However, the computational demands of deep learning
models pose energy challenges. Improving energy efficiency is vital for battery-
powered devices in healthcare, agriculture, and smart infrastructure.

2. Techniques for Energy Efficiency

Model Compression: Pruning and quantization reduce model size and computational
load. For instance, quantizing weights to 8-bit integers can reduce energy use by
over 60% in some models.

Efficient Architectures: Lightweight models like MobileNet and TinyML are


designed for low-resource environments while maintaining acceptable accuracy.

Hardware Acceleration: Specialized chips like Google’s Edge TPU and NVIDIA’s
Jetson series offer optimized inference with minimal power consumption.

Dynamic Voltage and Frequency Scaling (DVFS): Adjusting processing frequency


and voltage based on workload helps balance performance and power use.

3. Case Study: Smart Agriculture Sensor Node


An edge AI system using a low-power ARM Cortex-M4 microcontroller with a quantized
neural network was deployed to classify soil moisture levels. Power consumption was
reduced by 45% through model pruning and clock gating techniques, allowing multi-
day operation on a coin cell battery.

4. Conclusion
Energy-efficient AI at the edge requires a multifaceted approach combining software
and hardware optimizations. Ongoing research is focused on adaptive AI systems that
self-tune energy profiles based on context.

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