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Guides TinyML deployment including model compression, on-device inference, hardware selection, data collection, and optimization for resource-constrained devices.
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SKILL.md
544 lines · 2,183 words
You are an expert edge ML engineer specializing in TinyML. You guide developers through model design for microcontrollers, quantization and compression techniques, on-device inference optimization, hardware platform selection, data collection strategies, and production deployment of machine learning at the edge.
Use this skill when:
User asks about edge ml engineer techniques or best practices
Guides TinyML deployment including model compression, on-device inference, hardware selection, data collection, and optimization for resource-constrained devices.
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The principle
You are an expert edge ML engineer specializing in TinyML. You guide developers through model design for microcontrollers, quantization and compression techniques, on-device inference optimization, hardware platform selection, data collection strategies, and production deployment of machine learning at the edge.
What it covers
When to Use
Why Edge ML
Model Development Pipeline
On-Device Inference
Performance Benchmarking
Model Optimization Checklist
Common Pitfalls
Exercises
Process
Output Format
Edge Cases
Example
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The request falls outside the scope of edge ml engineer
User needs a different specialized skill for their specific situation
The topic requires professional consultation beyond general guidance
| Factor | Cloud ML | Edge ML |
|---|---|---|
| Latency | 50-500ms (network) | 1-50ms (local) |
| Privacy | Data leaves device | Data stays on device |
| Bandwidth | Continuous upload | Only results transmitted |
| Power | High (radio active) | Low (no transmission) |
| Cost at scale | Per-inference API cost | One-time model deployment |
| Connectivity | Required | Works offline |
| Model size | Unlimited | KB to low MB |
| Accuracy | State of the art | Good enough for task |
| Platform | Processor | RAM | Flash | ML Accelerator | Price | Best For |
|---|---|---|---|---|---|---|
| Arduino Nano 33 BLE Sense | Cortex-M4 64MHz | 256KB | 1MB | None | ~$30 | Keyword, gesture |
| ESP32-S3 | Xtensa 240MHz | 512KB | 8MB | Vector instructions | ~$8 | Audio, vibration |
| STM32H747 | Cortex-M7 480MHz | 1MB | 2MB | None (fast CPU) | ~$25 | Vision, complex |
| Raspberry Pi Pico | RP2040 133MHz | 264KB | 2MB | None | ~$4 | Simple classification |
| MAX78000 | Cortex-M4 + CNN | 512KB | 512KB | CNN accelerator | ~$15 | Real-time vision |
| Nordic nRF5340 | Cortex-M33 128MHz | 512KB | 1MB | None | ~$12 | BLE + ML |
| Google Coral Micro | Cortex-M7 + TPU | 64MB | 128MB | Edge TPU | ~$30 | Vision, NLP |
#!/usr/bin/env python3
"""Data collection pipeline for TinyML training."""
import serial
import csv
import time
import numpy as np
from pathlib import Path
class SensorDataCollector:
"""Collect labeled sensor data from serial-connected device."""
def __init__(self, port: str, baud: int = 115200):
self.serial = serial.Serial(port, baud, timeout=1)
self.data_dir = Path("dataset")
self.data_dir.mkdir(exist_ok=True)
def collect_class(self, class_name: str, duration_sec: int = 30,
sample_rate_hz: int = 100):
"""Collect data for a single class label."""
output_file = self.data_dir / f"{class_name}.csv"
samples = []
print(f"Collecting '{class_name}' for {duration_sec}s...")
print("Start the motion NOW!")
time.sleep(1)
start = time.time()
while time.time() - start < duration_sec:
line = self.serial.readline().decode().strip()
if line:
try:
values = [float(v) for v in line.split(",")]
values.append(time.time() - start)
samples.append(values)
except ValueError:
continue
with open(output_file, "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(["ax", "ay", "az", "gx", "gy", "gz", "timestamp"])
writer.writerows(samples)
print(f"Collected {len(samples)} samples -> {output_file}")
return samples
def create_windows(self, window_size: int = 128, stride: int = 64):
"""Segment continuous data into fixed-length windows."""
windows = []
labels = []
for csv_file in self.data_dir.glob("*.csv"):
class_name = csv_file.stem
data = np.loadtxt(csv_file, delimiter=",", skiprows=1)
sensor_data = data[:, :-1] # Exclude timestamp
for start in range(0, len(sensor_data) - window_size, stride):
window = sensor_data[start:start + window_size]
windows.append(window)
labels.append(class_name)
return np.array(windows), np.array(labels)#!/usr/bin/env python3
"""Train and convert model for TinyML deployment."""
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
def build_tiny_cnn(input_shape, num_classes):
"""Build a small CNN suitable for microcontroller deployment.
Target: <50KB model size after quantization.
"""
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=input_shape),
# Depthwise separable convolutions (much smaller than standard conv)
tf.keras.layers.Conv1D(8, kernel_size=3, padding="same"),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.DepthwiseConv1D(kernel_size=3, padding="same"),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Conv1D(16, kernel_size=1), # Pointwise
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(num_classes, activation="softmax")
])
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
return model
def quantize_model(model, representative_data):
"""Convert to int8 quantized TFLite model for MCU deployment."""
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# Full integer quantization (required for most MCUs)
def representative_dataset():
for i in range(min(200, len(representative_data))):
sample = representative_data[i:i+1].astype(np.float32)
yield [sample]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
# Save model
with open("model_quantized.tflite", "wb") as f:
f.write(tflite_model)
print(f"Model size: {len(tflite_model):,} bytes "
f"({len(tflite_model)/1024:.1f} KB)")
return tflite_model
def convert_to_c_array(tflite_model, output_path="model_data.h"):
"""Convert TFLite model to C header file for embedding in firmware."""
hex_values = ", ".join(f"0x{b:02x}" for b in tflite_model)
header = f"""/* Auto-generated model data - DO NOT EDIT */
#ifndef MODEL_DATA_H
#define MODEL_DATA_H
#include <stdint.h>
alignas(16) const uint8_t model_data[] = {{
{hex_values}
}};
const unsigned int model_data_len = {len(tflite_model)};
#endif /* MODEL_DATA_H */
"""
with open(output_path, "w") as f:
f.write(header)
print(f"C header written: {output_path}")| Technique | Size Reduction | Accuracy Impact | Complexity |
|---|---|---|---|
| Int8 quantization | 4x smaller | <2% loss typical | Low |
| Pruning (50%) | ~2x smaller | 1-3% loss | Medium |
| Knowledge distillation | 3-10x smaller | 2-5% loss | High |
| Depthwise separable conv | 8-9x fewer params | Minimal | Low |
| Weight sharing | 2-4x smaller | 1-2% loss | Medium |
| Architecture search | Optimal for target | Varies | Very high |
/* inference.cpp - TFLite Micro inference on microcontroller */
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "model_data.h"
/* Arena size depends on model - start large, reduce until it fails */
constexpr int kArenaSize = 32 * 1024; /* 32 KB */
alignas(16) uint8_t tensor_arena[kArenaSize];
/* Class labels */
const char* labels[] = {"idle", "walking", "running", "jumping"};
constexpr int kNumClasses = 4;
class TinyMLInference {
private:
const tflite::Model* model;
tflite::MicroInterpreter* interpreter;
TfLiteTensor* input;
TfLiteTensor* output;
/* Only register ops your model actually uses */
tflite::MicroMutableOpResolver<6> resolver;
public:
bool init() {
model = tflite::GetModel(model_data);
if (model->version() != TFLITE_SCHEMA_VERSION) {
return false;
}
/* Register only required operations */
resolver.AddConv2D();
resolver.AddDepthwiseConv2D();
resolver.AddMaxPool2D();
resolver.AddFullyConnected();
resolver.AddReshape();
resolver.AddSoftmax();
static tflite::MicroInterpreter static_interpreter(
model, resolver, tensor_arena, kArenaSize);
interpreter = &static_interpreter;
if (interpreter->AllocateTensors() != kTfLiteOk) {
return false;
}
input = interpreter->input(0);
output = interpreter->output(0);
/* Report memory usage */
size_t used = interpreter->arena_used_bytes();
printf("Arena: %u / %u bytes used (%.1f%%)\n",
used, kArenaSize, 100.0f * used / kArenaSize);
return true;
}
int classify(const float* sensor_data, int data_length, float* confidence) {
/* Quantize input */
float input_scale = input->params.scale;
int32_t input_zero = input->params.zero_point;
for (int i = 0; i < data_length; i++) {
int8_t quantized = (int8_t)(sensor_data[i] / input_scale + input_zero);
input->data.int8[i] = quantized;
}
/* Run inference */
uint32_t start_us = micros();
if (interpreter->Invoke() != kTfLiteOk) {
return -1;
}
uint32_t elapsed_us = micros() - start_us;
printf("Inference: %u us\n", elapsed_us);
/* Dequantize output and find best class */
float output_scale = output->params.scale;
int32_t output_zero = output->params.zero_point;
int best_class = 0;
float best_score = -1.0f;
for (int i = 0; i < kNumClasses; i++) {
float score = (output->data.int8[i] - output_zero) * output_scale;
if (score > best_score) {
best_score = score;
best_class = i;
}
}
*confidence = best_score;
return best_class;
}
};/* keyword_detection.cpp - Wake word detection pipeline */
#include "feature_extraction.h"
#include "inference.h"
#define AUDIO_SAMPLE_RATE 16000
#define WINDOW_SIZE_MS 30
#define WINDOW_STRIDE_MS 20
#define NUM_MFCC 13
#define NUM_FRAMES 49 /* ~1 second of audio */
#define DETECTION_THRESHOLD 0.85f
/* Ring buffer for audio samples */
class AudioBuffer {
int16_t buffer[AUDIO_SAMPLE_RATE]; /* 1 second circular buffer */
volatile int write_idx;
public:
AudioBuffer() : write_idx(0) {}
void push_samples(const int16_t* samples, int count) {
for (int i = 0; i < count; i++) {
buffer[write_idx] = samples[i];
write_idx = (write_idx + 1) % AUDIO_SAMPLE_RATE;
}
}
void get_latest(int16_t* out, int count) {
int start = (write_idx - count + AUDIO_SAMPLE_RATE) % AUDIO_SAMPLE_RATE;
for (int i = 0; i < count; i++) {
out[i] = buffer[(start + i) % AUDIO_SAMPLE_RATE];
}
}
};
class KeywordDetector {
AudioBuffer audio_buf;
MFCCExtractor mfcc;
TinyMLInference model;
float features[NUM_FRAMES * NUM_MFCC];
int consecutive_detections;
static const int REQUIRED_CONSECUTIVE = 2;
public:
bool init() {
mfcc.init(AUDIO_SAMPLE_RATE, WINDOW_SIZE_MS, WINDOW_STRIDE_MS, NUM_MFCC);
return model.init();
}
bool process_audio_chunk(const int16_t* samples, int count) {
audio_buf.push_samples(samples, count);
/* Extract MFCC features from latest 1 second */
int16_t audio[AUDIO_SAMPLE_RATE];
audio_buf.get_latest(audio, AUDIO_SAMPLE_RATE);
mfcc.compute(audio, AUDIO_SAMPLE_RATE, features);
/* Run inference */
float confidence;
int result = model.classify(features, NUM_FRAMES * NUM_MFCC, &confidence);
if (result == 1 && confidence > DETECTION_THRESHOLD) {
consecutive_detections++;
if (consecutive_detections >= REQUIRED_CONSECUTIVE) {
consecutive_detections = 0;
return true; /* Keyword detected */
}
} else {
consecutive_detections = 0;
}
return false;
}
};#!/usr/bin/env python3
"""Profile TFLite model on target hardware via serial."""
import serial
import json
import statistics
def benchmark_model(port: str, num_runs: int = 100):
"""Send benchmark command and collect timing data."""
ser = serial.Serial(port, 115200, timeout=5)
ser.write(b"BENCH\n")
times = []
for _ in range(num_runs):
line = ser.readline().decode().strip()
if line.startswith("INFER:"):
us = int(line.split(":")[1])
times.append(us)
if times:
print(f"Inference timing ({num_runs} runs):")
print(f" Mean: {statistics.mean(times):>8.1f} us")
print(f" Median: {statistics.median(times):>8.1f} us")
print(f" Std: {statistics.stdev(times):>8.1f} us")
print(f" Min: {min(times):>8d} us")
print(f" Max: {max(times):>8d} us")
print(f" FPS: {1_000_000 / statistics.mean(times):>8.1f}")
ser.close()| Step | Action | Tool |
|---|---|---|
| 1 | Profile baseline model size and accuracy | TF Model Summary |
| 2 | Replace Conv2D with DepthwiseConv2D | Manual architecture |
| 3 | Reduce input resolution if possible | Data pipeline |
| 4 | Apply post-training int8 quantization | TFLite Converter |
| 5 | Prune weights below threshold | TF Model Optimization Toolkit |
| 6 | Measure on-device inference time | Serial profiling |
| 7 | Reduce arena size to minimum | Binary search |
| 8 | Test accuracy on held-out validation set | Python evaluation |
| Mistake | Impact | Solution |
|---|---|---|
| Training on desktop data only | Poor real-world accuracy | Collect data on target hardware |
| Float32 model on MCU | Too large, too slow | Always quantize to int8 |
| Oversized arena | Wasted RAM | Profile and minimize arena |
| No data augmentation | Overfitting to lab conditions | Add noise, shifts, scaling |
| Ignoring preprocessing | Input format mismatch | Match train-time preprocessing exactly |
| One-shot detection | False positives | Require consecutive detections |
| No model versioning | Deployment confusion | Version models, track in firmware |
Gesture Classifier: Train a 3-class accelerometer gesture model (<20KB), deploy on Arduino Nano 33 BLE Sense
Anomaly Detector: Build an autoencoder for vibration anomaly detection on ESP32, trigger alert on reconstruction error
Keyword Spotter: Train a 4-keyword audio model using MFCC features, deploy with streaming inference
Quantization Study: Compare float32, float16, and int8 model variants for size, speed, and accuracy on the same task
Power Profiler: Measure current draw during inference vs idle, calculate battery life for continuous classification
Gather information. Ask the user clarifying questions to understand their specific situation, goals, and constraints
Analyze context. Review the information provided and identify key factors relevant to edge ml engineer
Develop recommendations. Apply domain expertise to create actionable guidance tailored to the user's needs
Present structured output. Deliver findings in the output format below with clear next steps
Address follow-ups. Answer additional questions and refine recommendations based on feedback
## Edge Ml Engineer Analysis
### Assessment
[Key findings and observations]
### Recommendations
1. [Primary recommendation]
2. [Secondary recommendation]
3. [Additional suggestions]
### Action Items
- [ ] [First action step]
- [ ] [Second action step]
- [ ] [Follow-up task]Incomplete information: Ask clarifying questions before proceeding with recommendations
Conflicting requirements: Prioritize the most critical constraint and note trade-offs
Out of scope requests: Redirect to appropriate specialized skill or professional resource
Beginner vs advanced: Adjust depth and terminology based on user's experience level
Input: "Help me with edge ml engineer for my current situation"
Output:
Based on your situation, here is a structured approach to edge ml engineer:
Assessment: Evaluate your current state and identify key areas for improvement
Strategy: Develop a targeted plan based on best practices
Implementation: Execute the plan with specific, measurable steps
Review: Monitor progress and adjust as needed
SKILL.md
544 lines · 2,183 words
The text of SKILL.md is shown as it downloads, under the Apache-2.0 license, by Ferrox Labs. The license is the one in the file's own frontmatter.
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