Choosing AI Accelerators for Robots

3 months ago 14

Hi everyone!
I’m Anton, and on my channel, I usually talk about different Edge AI boards. But today, I want to focus specifically on how these boards can be used in robotics applications. So, let’s dive in — because choosing the right AI board for a robot can be surprisingly complex.

This article is also available as a video:

In most cases, the best “Edge” platform for a robot is actually a full-fledged computer with a dedicated GPU. This could be a cloud-based setup or a local workstation. But when does this approach fail?

Limited Connectivity: Robots operating in remote environments, such as delivery robots in rural areas, may lack stable network access — making cloud solutions impractical. Also, high-frame-rate processing (like 500 FPS) can’t be efficiently offloaded over any network — cloud or local.
On-Premises Network Saturation: In facilities with thousands of robots (e.g., large warehouses), network bandwidth may be insufficient to support cloud processing for every unit.
Budget Constraints: You probably won’t install NVIDIA A100 GPUs in every robot due to cost.
Power Consumption Limits: Battery-powered or outdoor robots often can’t support the energy draw of high-end GPUs.

When powerful GPUs aren’t an option, NVIDIA Jetson boards are often the next best solution. Prices start around $400 and can easily climb into the thousands, depending on the model (e.g., Orin NX, Orin AGX).

Strengths of Jetson:

Full CUDA GPU available for tasks like PyTorch inference.
TensorRT optimization support for further acceleration.

Limitations of Jetson:

Not budget-friendly compared to many alternatives.
Jetsons aren’t fully production-ready for some use cases:
Kernel components are partially proprietary.
Hardware interfaces can introduce unpredictable video latency or framerate issues.
The integrated NVIDIA Deep Learning Accelerator (DLA) cores, available from the NX series onward, are quite limited in performance compared to the main GPU.

Beyond NVIDIA, the next tier includes boards based on Intel, Qualcomm, and AMD chipsets. All three companies now brand their solutions as “AI-ready,” though this should be taken with some skepticism.

Intel and Qualcomm typically offer better AI software support than AMD, especially for vision-language models (VLMs), large language models (LLMs), and vision-language-action models (VLAs). Intel likely has the strongest ecosystem as of now, followed by Qualcomm, with AMD trailing.
These chips integrate relatively capable NPUs (Neural Processing Units), often outperforming NVIDIA’s DLA cores in certain tasks.

However, limitations remain:

AI support is still not as mature as NVIDIA’s CUDA ecosystem.
Hardware integration and video input handling may still pose challenges, similar to Jetson platforms.
Pricing typically starts from $500 and up.

Here I tried to combine all numbers in a simple table:

Zoom image will be displayed

Table for Intel, AMD, Qualcom, Jetsons (by author)

For those targeting lower costs or simpler tasks, there’s a vast category of specialized AI accelerators, generally based on NPUs without full GPU support. These are excellent for tasks like object detection and image classification but have key limitations:

Poor or no support for VLM, LLM, or VLA models.
Lack of capability for tasks like stereo depth estimation, which generally requires GPU support.

If your robot doesn’t need advanced perception models or 3D scene understanding, these accelerators are often ideal.

Popular NPU-Based Solutions:

M.2 Form Factor Accelerators:

Hailo (Hailo-8)
Axilera
DeepX
SimiAI
Others

These plug directly into M.2 slots and typically outperform integrated NPUs in SoCs.

Integrated Accelerators in SoCs:

Rockchip (e.g., RK3588 with its NPU)
Texas Instruments
Sophon (e.g., BM1684 series)
Various Chinese vendors are producing efficient yet low-cost options.

Here is a simple table to highlight these options:

Table for NPUs

I categorize these accelerators along two dimensions:

Speed:

M.2 accelerators tend to offer higher inference speeds compared to integrated NPUs.

Model Support:

Many NPUs can handle standard CNN-based tasks.
Support for modern architectures like transformers, LLMs, VLMs, or VLA models is rare.

In most cases, if your application is limited to detection, classification, or basic geometric evaluations, NPUs are sufficient. These workloads cover about 90% of robotics tasks today.

Choosing the right AI accelerator depends on: