Benchmark NPUs for an Autonomous Forklift Vision Stack
Overview
What this challenge is about.
Convert three ONNX models for an autonomous forklift, benchmark latency, power, and accuracy on three NPUs, then write a procurement memo. Earn a verifiable certificate.
The scenario
The scale-up (around 120 people, deploys around 600 forklifts per year across European warehouses) ships its vision board on a 4-year hardware refresh cycle, so a wrong NPU pick locks in over EUR 5 million of opportunity cost.
The Brief
What you'll do, and what you'll demonstrate.
Pick the right NPU for a 4-year autonomous-forklift program by benchmarking 3 chips on the actual perception workload and writing a defensible procurement memo.
Earning criteria — what you'll demonstrate
- Convert and deploy ONNX models across heterogeneous NPU SDKs
- Profile end-to-end pipeline latency, not just per-model latency
- Build a defensible vendor scoring matrix with sensitivity analysis
- Communicate procurement trade-offs to executive leadership
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Edge ML and On-Device Machine Learning
Master · Ai Systems
Strong alignment
This challenge maps to Edge ML and On-Device Machine 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.
- Edge Inference
Apply edge inference to solve real industry problems and demonstrate production-level capability.
- Npu Benchmarking
Apply npu benchmarking to solve real industry problems and demonstrate production-level capability.
- Onnx
Apply onnx to solve real industry problems and demonstrate production-level capability.
- Model Deployment
Apply model deployment to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Vendor Evaluation
Apply vendor 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:
AI Solutions Architect
Owning a multi-vendor NPU benchmark plus a defensible procurement memo is exactly the kind of high-stakes architecture decision AI solutions architects ship at scale-ups.
This challenge sharpens
- edge-inference
- npu-benchmarking
- vendor-evaluation
MLOps Engineer
Building a reproducible cross-SDK benchmark harness is core MLOps work that transfers to any team shipping ML to heterogeneous hardware.
This challenge sharpens
- onnx
- benchmarking
- model-deployment
Machine Learning Engineer
End-to-end pipeline profiling and accuracy retention checks across deployment targets is bread-and-butter MLE work at any edge-AI company.
This challenge sharpens
- edge-inference
- benchmarking
- onnx