Benchmark NPUs for an Autonomous Forklift Vision Stack
Overview
What this challenge is about.
Benchmark NPUs for an Autonomous Forklift Vision Stack. Advanced challenge in analysis. Analyzing real datasets and building models that drive decisions, ear...
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.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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