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title: Unlocking Autonomy in Military Drones Goes Beyond SWaP-C
description: Rising defense budgets are pushing military drone makers toward on-board AI inference rather than cloud-based decision-making, driven by GPS/GNSS jamming...
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# Unlocking Autonomy in Military Drones Goes Beyond SWaP-C

**[Embedded.com](https://daily.dev/sources/embedded-com)** · 8 min read · 0 upvotes · 0 comments

## Summary

Rising defense budgets are pushing military drone makers toward on-board AI inference rather than cloud-based decision-making, driven by GPS/GNSS jamming threats seen in Ukraine. Lantronix CEO Saleel Awsare explains that space, weight, power, and cost (SWaP-C) constraints limit how much edge compute can be added, with SOMs built on Qualcomm's AI engine and sub-7nm process nodes balancing performance against power draw across entry-level, mid-range, and high-end TOPS tiers. Reliability standards like MIL-STD-810, MAVLink 2.0, and DO-178C's design assurance levels further shape adoption, requiring disaggregation of flight-stabilization loops from AI inference tasks. Modular, pin-compatible SOM designs let defense customers upgrade hardware alongside software as mission data demands grow.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.embedded.com/unlocking-autonomy-in-military-drones-goes-beyond-swap-c>

## Questions this post answers

### What is SWaP-C in the context of military drone design?

SWaP-C stands for space, weight, power, and cost, the set of physical and budgetary constraints that limit how much computational hardware can be added to a drone. Components for flight, navigation, combat, and logistics leave limited room for edge AI compute, and these constraints vary across different UAV/drone classes.

_Engineers weighing edge AI trade-offs for constrained hardware can follow SWaP-C design discussions on daily.dev._

### Why do military drones need on-board AI instead of relying on cloud-based processing?

On-board AI is needed because remotely operating large numbers of UAVs is labor-intensive and jamming systems can quickly de-weaponize drones that depend on GPS/GNSS or cloud connectivity. Equipping drones with real-time, edge-based decision-making lets them keep functioning in GPS- and GNSS-denied environments where communication links may be disrupted.

_Track how reliability pressures are reshaping edge AI architecture decisions on daily.dev._

### Why can't AI inference software just run on the same processor as a drone's flight controller under DO-178C?

Under DO-178C, mixing software of different design assurance levels (DALs) on the same processor without strict disaggregation forces the entire system to be certified to the highest level, DAL-A. Since certifying a large black-box AI model to DAL-A is prohibitively expensive and complex, developers isolate DAL-A flight-stabilization loops from lower-DAL inference tasks instead.

_Developers navigating safety-critical certification trade-offs can follow embedded systems coverage on daily.dev._

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---

Tags: [#embedded](https://daily.dev/tags/embedded), [#local-ai](https://daily.dev/tags/local-ai)

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