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!!!!!