Hey, recently I’ve been tinkering with something fun — running machine learning on those tiny IoT devices! Seeing the number “1KB”, many people shake their heads: how is that possible? Indeed, a high-definition photo takes several MB, so where’s the magic that allows AI to fit into such a tiny space?
Actually, TinyML is such a magical field. It allows machine learning models to run happily on micro devices with only a few KB of memory! Just think about it, it’s like putting an elephant in a refrigerator — it sounds impossible, but there is indeed a way.
tinyml
What is TinyML?
In simple terms, TinyML is a machine learning technology specifically designed for devices with extremely limited resources.
Those LoRa devices? They are super power-efficient and have long transmission distances, but their computing power and storage space are pitifully small — some even have only 1KB of RAM! With such little space, you can’t even fit a selfie, yet you want to make AI live there… It sounds like trying to make an elephant sleep in an ant’s nest, impossible!
But MicroTVM is designed for this! It is the little brother of the TVM family, specifically handling such “extreme challenges”.
microtvm
MicroTVM: The Model Slimming Master
Honestly, when I first saw MicroTVM, I was also stunned. This thing can compress models that are often dozens of MB down to a few hundred bytes?! It’s black technology…
MicroTVM is actually a model compilation optimization framework that can “translate” ordinary machine learning models into highly optimized binary code.
Let’s look at a simple example:
import tvm
from tvm import relay
# Import the pre-trained model (for example, a super small model for detecting simple gestures)
mod, params = relay.frontend.from_keras(tiny_model)
# Slimming magic begins
target = tvm.target.target.micro("stm32f4xx")
with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(
mod, target, params=params)
# Create a deployment package for LoRa devices
micro = tvm.micro.create_micro_mod(lib, target)
Looks simple, right? Hehe, there’s a lot of black magic behind it: quantization, weight pruning, knowledge distillation… MicroTVM uses these techniques to “squeeze out the moisture” from the model, leaving only the essence.
lora1kb
The 1KB Miracle on LoRa Devices
Some may ask: what can you do with 1KB? Don’t underestimate this 1K!
In fact, after extreme optimization, a 1KB model can:
Detect simple actions (like falls); recognize a few basic sounds (doorbell, alarm); and even perform basic anomaly detection! Although it can’t distinguish between a cat and a dog, it’s more than enough to detect if there’s any movement.
The code for deployment on LoRa devices looks something like this:
// Run inference on LoRa device
void run_inference() {
// Only requires 1KB of model data!
static const unsigned char model_data[] = {...};
// Initialize TVM runtime
TVMMicroRuntime runtime;
runtime.Init(model_data, sizeof(model_data));
// Input sensor data
float input_data[8] = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8};
runtime.SetInput(0, input_data);
// Run inference
runtime.Run();
// Get results
float output[2];
runtime.GetOutput(0, output);
// Decide whether to send an alert based on output
if (output[1] > 0.8) {
send_lora_alert();
}
}
Don’t be intimidated by this code! This is already a highly simplified version — the real code would do more optimizations based on your hardware.
1
Real-World Applications
There are so many! Soil monitoring in farmland, sending data only when anomalies occur, saving power! Remote wildlife monitoring, activating cameras only when animals are detected, our project team saved a lot of money using this. There’s also a fault warning system for factory equipment; once deployed, it can sense when equipment is about to fail.
Friendly reminder: always thoroughly test model accuracy before deployment! After all, we are compressing the model with a cheat, and there will definitely be some loss in accuracy, so it must be confirmed to be within an acceptable range. Don’t end up with a system that has a super high false alarm rate, or it will be a joke.
Also, don’t start with something complex; begin with the simplest binary classification tasks, like anomaly/no anomaly, as these models are the easiest to compress to an extremely small size.
The field of TinyML is developing super fast… what was “impossible” yesterday is “a piece of cake” today. However, to do AI in such constrained environments, you need a somewhat “extreme” optimization spirit; otherwise, how can you fit an elephant in a refrigerator? Haha!
Wishing everyone success and good health.
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