Selected engineering work
Neural networks on microcontrollers
Verified active years: 2024
FastestDet architecture refined for a 35% accuracy gain; real-time detection deployed on Renesas MCUs and Sony IMX500 smart cameras — AI where there is no cloud to fall back on.
The problem
Object detection on a microcontroller has to operate within the target device’s constraints. The challenge was to refine a lightweight network and carry it through training, evaluation and deployment readiness for on-device use.
My contribution
AI / ML systems engineer
Led architectural refinement of FastestDet for ultra-lightweight deployment and built the surrounding optimisation, training and evaluation pipelines. Deployed real-time detection models on Renesas microcontrollers.
Team / platform context
My documented responsibility was model architecture and the model-development pipeline through deployment readiness. This case study does not claim ownership of the complete hardware or embedded product.
Refine the network for the deployment target
I led architectural refinement of the FastestDet object-detection network for ultra-lightweight deployment. The documented outcome was a 35% accuracy improvement and real-time detection deployed on microcontrollers.
Own the model-development work
My contribution included the surrounding optimisation, training and evaluation pipelines, through deployment readiness. This connected the architecture work to execution on the target device.
Keep the result in context
The result describes this project’s model work. It does not establish a general benchmark across devices or datasets. Separate embedded-vision work is covered in the embedded vision project record.
Engineering decisions
Refine a lightweight detection architecture
My work focused on architectural refinement of FastestDet for ultra-lightweight deployment. The documented result was an accuracy improvement within that project; no comparison against unreported alternative models is implied.
Carry model changes through the development pipeline
I built the surrounding optimisation, training and evaluation pipelines through deployment readiness. This connects architecture work to the practical requirement of running detection on the target device.
Outcomes & evidence
- Achieved the reported 35% accuracy improvement through FastestDet architectural refinement.
- Deployed real-time detection models on Renesas microcontrollers.
- Built model-development pipelines covering optimisation, training, evaluation and deployment readiness.
The 35% improvement is the result recorded in the existing project record and public resume. The baseline, dataset and evaluation protocol are not published here, so it should not be treated as an independently reproducible benchmark or a percentage-point gain.
Engineering takeaway
The deployment target belongs in the model-development loop. Architecture, training, evaluation and deployment readiness need to be considered together when compute resources are constrained.