Bravo
If ARM was an arm, BRN would be its biceps💪!
Overall Efficiency: 280%+ improvement in accuracy-per-energy ratio!
	
		
			
		
		
	
		
		
	
	
		
	
	
		
			
		
		
	
Overall Efficiency: 280%+ improvement in accuracy-per-energy ratio!!!!!
 It would appear the gentlemen below from VW running more projects with Akida. Uploaded to GitHub yesterday.
Results look strong.
Fernando Sevilla Martínez
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GitHub - SevillaFe/EcoEdgeAI-akida-mac: A comprehensive workflow for comparing energy efficiency between conventional hardware (Mac M-series GPU/CPU) and neuromorphic hardware (Akida on Raspberry Pi 5) for autonomous driving steering angle prediction
A comprehensive workflow for comparing energy efficiency between conventional hardware (Mac M-series GPU/CPU) and neuromorphic hardware (Akida on Raspberry Pi 5) for autonomous driving steering ang...github.com
SevillaFe/EcoEdgeAI-akida-macPublic
A comprehensive workflow for comparing energy efficiency between conventional hardware (Mac M-series GPU/CPU) and neuromorphic hardware (Akida on Raspberry Pi 5) for autonomous driving steering angle prediction.
SevillaFe/EcoEdgeAI-akida-mac
Name SevillaFe
yesterday
LICENSE yesterday README.md yesterday requirements_mac.txt yesterday requirements_rpi5.txt yesterday workflow_guide.md yesterday Repository files navigation
EcoEdgeAI-akida-mac
A comprehensive workflow for comparing energy efficiency between conventional hardware (Mac M-series GPU/CPU) and neuromorphic hardware (Akida on Raspberry Pi 5) for autonomous driving steering angle prediction.
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This project provides a complete pipeline to:
Project Overview
- Train deep learning models (PilotNet, LaksNet, MiniNet) for steering angle prediction
 - Benchmark inference performance on conventional hardware with CodeCarbon energy tracking
 - Convert models to neuromorphic format using Akida
 - Benchmark neuromorphic inference with TC66 USB power meter
 - Generate comprehensive eco-efficiency comparisons
 Key Research Questions
- How much energy does neuromorphic computing save?
 - What is the accuracy trade-off?
 - What is the latency difference?
 - Which architecture is most efficient for edge deployment?
 Our experiments show:
Results Preview
- Energy Efficiency: Up to 76% reduction in energy consumption per inference
 - Latency: 40-50% faster inference on neuromorphic hardware
 - Accuracy: Minimal degradation (<10% MSE increase)
 - Overall Efficiency: 280%+ improvement in accuracy-per-energy ratio
 
Hardware Requirements
Mac (Training & Benchmarking)
- MacBook with M-series processor (M1/M2/M3)
 - 16GB+ RAM recommended
 - macOS 12.0+
 Raspberry Pi 5 (Neuromorphic Benchmarking)
- Raspberry Pi 5 (4GB/8GB)
 - BrainChip Akida neuromorphic processor board
 - TC66/TC66C USB power meter
 - 32GB+ microSD card
 - Active cooling recommended
 
Overall Efficiency: 280%+ improvement in accuracy-per-energy ratio!!!!!