Session
Poster Session 2
Location
Salt Palace Convention Center, Salt Lake City, UT
Abstract
MOTIVATION:
SmallSats are asked to make time-sensitive calls (vessel detection, cloud filtering, debris tracking) but CubeSat-class compute is starved for power, memory, and radiation-tolerant silicon.
Most missions still downlink raw imagery for ground processing, costing hours-to-days of latency and scarce bandwidth. Running inference onboard means solving two separate problems:
- When to run inference: compute and power budgets can’t tolerate a neural network firing on every frame.
- Whether it fits when it does run: the model has to live inside a memory and compute envelope measured in megabytes, not gigabytes.
Document Type
Event
Paper
Advanced AI Deployment on SmallSats: A Mixed Precision Approach to Onboard Perception and FPGA-Gated Inference
Salt Palace Convention Center, Salt Lake City, UT
MOTIVATION:
SmallSats are asked to make time-sensitive calls (vessel detection, cloud filtering, debris tracking) but CubeSat-class compute is starved for power, memory, and radiation-tolerant silicon.
Most missions still downlink raw imagery for ground processing, costing hours-to-days of latency and scarce bandwidth. Running inference onboard means solving two separate problems:
- When to run inference: compute and power budgets can’t tolerate a neural network firing on every frame.
- Whether it fits when it does run: the model has to live inside a memory and compute envelope measured in megabytes, not gigabytes.
