How to Optimize Model Inference on Edge Devices?

Posted by JaneDoe on September 16, 2025

Hello everyone,

I'm currently working on deploying a TensorFlow Lite model to a low‑power IoT device. While the model runs correctly, the inference latency is around 200 ms, which is too high for my real‑time requirements. I've tried quantization and model pruning, but there's still a noticeable lag.

Does anyone have experience with further optimization techniques? Specifically, I'm interested in:

Operator fusion and custom kernels
Using NNAPI or vendor‑specific delegates
Potential architectural changes to reduce overhead

Any code snippets, benchmark results, or best‑practice guides would be greatly appreciated!

Thanks in advance,

Jane

Comments (2)

MikeTech

Sep 16, 2025 at 2:23 PM

Quantization aware training usually gives better results than post‑training quantization. Also, try the TensorFlow Lite GPU delegate if your device has a GPU. It can cut latency by up to 50%.

AIWizard

Sep 17, 2025 at 9:10 AM

Consider converting parts of the model to a custom op using the Flex delegate. It lets you offload heavy operations to the CPU while keeping others on the accelerator.