Make or Buy:
Choosing the Right Compute Platform for Robotics Systems
Robotics teams must choose early whether to develop a custom compute board or adopt a modular compute platform. Compute platforms – MPUs, CPUs, GPUs, NPUs and hybrid accelerators – define latency, AI capability, determinism and system scalability.
The architectural decision directly influences time-to-market, engineering risk and product reliability.
A custom compute board promises absolute design freedom with tailored power architecture, tailored thermals, tailored interfaces. But it also places the full burden of power integrity, DDR routing, high-speed design, thermal management, and certification risk onto your engineering team.
Modular SOM/COM platforms take the opposite approach: compute is pre-validated, the software stack is ready to use, and integration risk is dramatically reduced. The trade-off is less freedom at board level, but a far faster and safer path to deployment.
Racing Ahead: Smarter Product Strategies
Market expansion of Edge AI is shaping development approaches and accelerating time-to-market in robotics and embedded systems.
CAGR
Omdia projects that the market for AI processors at the edge will grow from around 31 billion USD in 2022 to approximately 60 billion USD by 2028, increasing the opportunity cost of delayed product launches and slow platform decisions. Source: Omdia
billion USD
Recent market studies estimate the global Edge AI market in this range for 2024, putting strong pressure on robotics and embedded teams to shorten time-to-market and reduce development risk. Source: Polaris Market Research
The Core Dilemma: Freedom vs. Safety
Every robotics team faces a critical decision: build a custom solution from the ground up or leverage proven, modular platforms developed by others.
This is not a question of “Can we build it?” It’s a question of “Is building it the smartest, fastest and safest way to ship a reliable robotics system?”
Hardware
Software
Effort (Time & Cost)
MAKE
Compute Board (Chip-Down)
Full PCB design: power tree, DDR, high-speed routing; full responsibility for SI/PI, EMI/EMC custom thermal & mechanical design selection and qualification of MPU/CPU/NPU, PMICs, memory.
BSP, bootloader & driver development from scratch; integration of GPU/NPU toolchains; long-term maintenance of firmware & OS stack.
6-12+ months including bring-up, debug, redesign cycles & certification; high NRE; high EE/FW/thermal workload; long risk tail.
BUY
Modular SOM/COM or System Platform
Pre-validated modules (SMARC, OSM, COM-Express, COM-HPC); tested power & memory layouts; reference carrier designs; predictable thermal profiles; vendor-validated bring-up.
Ready-to-use BSP, drivers & AI runtimes; pre-integrated NPU/GPU SDKs; vendor maintenance, patches and roadmap alignment.
1-3 months depending on carrier work; low NRE; predictable lifecycle; reduced certification burden; accelerated deployment.
The Five Essential Design Questions
Before committing to either path, teams should challenge their assumptions with a set of strategic questions that reveal hidden risks, internal limitations, and long-term implications:
- Is compute a differentiator of our product or simply enabling infrastructure?
- What is the business impact if the compute board slips by 3-6 months?
- Do we have in-house expertise for DDR, SI/PI and system-level validation?
- Will our roadmap require performance scaling or future silicon flexibility?
- How comfortable are we maintaining an entire BSP and AI stack ourselves?
Featured Solutions
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