Borland & Kade
Borland Borland
Kade Kade
Borland Borland
Hey Kade, have you checked out the new micro‑edge AI frameworks that let you run neural nets directly on tiny IoT chips? They’re pushing the limits of what small hardware can do, and I’ve been tinkering with a few to see how far we can get the inference speed up. What’s your take on squeezing more intelligence into the next generation of embedded systems?
Kade Kade
Nice find. Tiny chips doing heavy lifting? That’s the kind of frontier that keeps me up. I’m all about pushing the clock‑speed, pruning weights, slicing models until they fit in a grain of dust. If the inference rate starts lagging, I’ll cut the network in half or swap the activation for something simpler. The trick is staying restless enough to keep hunting for better ops‑codes, but focused enough to actually get the numbers right. Got any demos I can poke at?
Borland Borland
Sure thing, Kade. I’ve got a tiny demo on GitHub that runs a pruned MobileNet on a Raspberry Pi 4 using TensorRT Lite. The repo includes the model, a quick‑start script, and benchmarks that show you the trade‑offs when you cut the layers in half. Just clone the repo, install the dependencies, and run `python run_demo.py` – you’ll see the latency drop in real time. If you want to tweak the activations or experiment with INT8 quantization, the script’s already set up for that. Happy hunting!
Kade Kade
Nice, that’s exactly the kind of thing I want to dig into. Give me the link so I can spin it up right away and start trimming those layers. If the latency starts to lag, I’ll swap ReLU for a faster activation and throw in some INT8 tricks. Don’t hold back on the edge cases—those are where the magic happens. Let’s see how far we can push that Pi.
Borland Borland
Here’s the link to the repo you can clone right away: https://github.com/borland/tiny-mobilenet-tensorrt Let me know how the trimming and INT8 tricks turn out on the Pi.
Kade Kade
Got it, pulling it down now. Will run the cuts and quantization and ping you once I’ve got numbers. Expect a few minutes of crunching, but it should be fast. Stay tuned.