Hardware Guide
Espressif's ESP32-C6 excels at anomaly detection via Edge Impulse. The 1-core risc-v at 160 MHz with 512 KB SRAM handles 15 KB quantized models with 16.0x RAM headroom. Built-in Wi-Fi 6 (802.11ax) enables wireless result reporting.
| Spec | ESP32-C6 |
|---|---|
| Processor | Single-core RISC-V @ 160 MHz |
| SRAM | 512 KB |
| Flash | Up to 4 MB (external) |
| Key Features | Wi-Fi 6 with OFDMA and TWT, Matter/Thread support via 802.15.4, RISC-V architecture, LP core for ultra-low-power operation, Hardware crypto acceleration |
| Connectivity | Wi-Fi 6 (802.11ax), Bluetooth 5 LE, 802.15.4 (Thread/Zigbee) |
| Price Range | $1 - $3 (chip), $5 - $15 (dev board) |
The ESP32-C6's 512 KB SRAM provides 16.0x the 32 KB minimum for anomaly detection. This generous headroom means the 15 KB model tensor arena, sensor input buffers, and application logic (vibration/current/temperature polling, Wi-Fi 6 (802.11ax) stack, state management) all fit without contention. The remaining 474 KB after model allocation supports complex application features. For firmware and model storage, the 4 MB flash comfortably houses the Edge Impulse runtime, the 15 KB model binary, application firmware, and OTA update partitions for field upgrades. Flash usage is well within budget for this configuration. The ESP32-C6 adds Wi-Fi 6 and 802.15.4 (Thread/Zigbee) to the RISC-V platform. The dual-radio capability enables Matter-compatible smart home ML applications. With 512 KB SRAM, it handles mid-complexity models comfortably. For anomaly detection, connect a vibration sensor (e.g., ADXL345 accelerometer via I2C/SPI) via SPI and a current sensor (e.g., ACS712 via ADC) via ADC and a temperature sensor (e.g., DS18B20 or TMP36 via ADC) via ADC to the ESP32-C6. Sample at 50-200 Hz and collect windows of 64-256 samples as model input. Compute FFT or statistical features in firmware before inference. Edge Impulse provides an end-to-end workflow: data collection from the ESP32-C6 via serial or WiFi, cloud-based training with auto-quantization, and deployment via C++ library export or Arduino library. The platform estimates on-device RAM and flash usage before deployment, reducing trial-and-error. Wi-Fi-connected boards can use the Edge Impulse daemon for direct data ingestion. At $1-3 per chip ($5-15 for dev boards), the ESP32-C6 offers strong value for anomaly detection deployments. Key ESP32-C6 features for this workload: Wi-Fi 6 with OFDMA and TWT, Matter/Thread support via 802.15.4, RISC-V architecture, LP core for ultra-low-power operation, Hardware crypto acceleration.
Create Edge Impulse project for ESP32-C6
Sign up at edgeimpulse.com and create a new project for anomaly detection. Install the Edge Impulse CLI (npm install -g edge-impulse-cli). Connect the ESP32-C6 board directly via the EI firmware image, or the data forwarder to stream vibration data from your Espressif development board.
Collect vibration training data
Connect a vibration sensor (e.g., ADXL345 accelerometer via I2C/SPI) and current sensor (e.g., ACS712 via ADC) and temperature sensor (e.g., DS18B20 or TMP36 via ADC) to the ESP32-C6 via I2C. Use Edge Impulse's data forwarder or direct board connection to stream samples to the cloud. Collect 500+ labeled samples across all classes. Include normal operating conditions and edge cases in your dataset.
Train model in Edge Impulse Studio
Design an impulse with the appropriate signal processing block (raw data processing). Add a autoencoder (3-4 dense layers) learning block. Train and evaluate — Edge Impulse shows estimated latency and memory usage for the ESP32-C6. Target under 12 KB model size and under 30 KB peak RAM.
Deploy and validate on ESP32-C6
Deploy via Edge Impulse CLI (edge-impulse-cli export) or download the C++ library. Allocate a tensor arena of 23-38 KB in a static buffer. Run inference on live vibration data and compare predictions against your test set. Report results via MQTT or HTTP for remote validation. Measure inference latency and peak RAM usage to verify they meet application requirements.
Espressif xtensa-lx7 at 240 MHz with 512 KB SRAM. $3-8 per chip. Excellent rated.
Nordic Semiconductor cortex-m4f at 64 MHz with 256 KB SRAM. $5-8 per chip. Compared to ESP32-C6: less RAM but lower cost. Excellent rated.
STMicroelectronics cortex-m4f at 168 MHz with 192 KB SRAM. $3-10 per chip. Compared to ESP32-C6: less RAM but lower cost. Excellent rated.
Connect sensors to AI inference — design monitoring workflows visually and compile to firmware for continuous edge detection.
Get Started Free