AI & Machine Learning

Embedded SLAM Acceleration on GPU and FPGA

A technical project template for porting SLAM workloads to embedded GPU and FPGA hardware with benchmarks and power-aware tradeoffs.

SLAMJetsonFPGACUDAKria K26

Architecture

SLAM acceleration work starts by identifying hot paths: scan matching, map updates, point cloud filtering, and transform operations. Each path can be benchmarked on CPU, GPU, and FPGA implementations.

Benchmark notes

Benchmarks should record dataset, sensor rate, map size, memory behavior, and thermal conditions. Without that context, performance numbers are hard to compare.

Documentation model

Project pages can include architecture diagrams, profiling screenshots, benchmark tables, source links, and hardware configuration notes.