Quantize a CNN for Battery-Powered Wildlife Cameras at a Climate Nonprofit
Overview
What this challenge is about.
Quantize a CNN from FP32 to int8 for a wildlife camera running on a Cortex-M, measuring accuracy, latency, and energy per inference. Get a verifiable certificate.
The scenario
The nonprofit (around 25 staff, around 800 cameras deployed across Iberian Peninsula sites) loses around 30% of cameras per year to battery drain caused by inefficient inference.
The Brief
What you'll do, and what you'll demonstrate.
Quantize the species classifier to fit the 4 MB / 200 ms / 30 mJ budget while keeping top-1 accuracy within 2 percentage points of the FP32 baseline.
Earning criteria — what you'll demonstrate
- Apply post-training and quantization-aware training methods on real CNNs
- Reason about size/latency/energy budgets jointly
- Run on-device-style profiling (or documented proxy)
- Communicate quantization trade-offs to a firmware audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Learning
Master · Deep Learning
Strong alignment
This challenge maps to Deep Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Quantization
Apply quantization to solve real industry problems and demonstrate production-level capability.
- Qat
Apply qat to solve real industry problems and demonstrate production-level capability.
- Edge Deployment
Apply edge deployment to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Energy Budgeting
Apply energy budgeting to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
MLOps Engineer
Edge-deployment quantization with explicit energy budgets is the kind of work MLOps engineers do at hardware-product or conservation-tech companies.
This challenge sharpens
- quantization
- edge-deployment
- energy-budgeting
Machine Learning Engineer
QAT and on-device inference profiling are increasingly part of the MLE job description on edge-AI teams.
This challenge sharpens
- quantization
- qat
- edge-deployment
Computer Vision Engineer
Shipping vision models inside tight hardware budgets is a recurring CV-engineer responsibility on hardware-product teams.
This challenge sharpens
- edge-deployment
- evaluation
- quantization