Anomaly Detection with Edge AI
Detecting unusual patterns in sensor data using lightweight autoencoders or statistical models. Learns normal operating behavior and flags deviations. Extremely resource-efficient — runs on the most constrained MCUs. Applicable to vibration, current, temperature, or any repetitive signal.
Hardware Requirements
| Minimum RAM | 32 KB |
| Minimum Flash | 256 KB |
| Sensor Inputs | vibration, current, temperature |
| Typical Model Size | 15 KB (quantized int8) |
Compatible Microcontrollers
ESP32
Espressif
520 KB RAM · 240 MHz
$5–$15 (dev board)
ESP32-C3
Espressif
400 KB RAM · 160 MHz
$4–$10 (dev board)
ESP32-C6
Espressif
512 KB RAM · 160 MHz
$5–$15 (dev board)
ESP32-S3
Espressif
512 KB RAM · 240 MHz
$10–$25 (dev board)
i.MX RT1062
NXP
1024 KB RAM · 600 MHz
$25–$40 (dev board)
nRF52833
Nordic Semiconductor
128 KB RAM · 64 MHz
$10–$25 (dev board)
nRF52840
Nordic Semiconductor
256 KB RAM · 64 MHz
$20–$35 (dev board)
RA6M5
Renesas
512 KB RAM · 200 MHz
$25–$50 (dev board)
STM32F4
STMicroelectronics
192 KB RAM · 168 MHz
$10–$30 (dev board)
STM32F7
STMicroelectronics
512 KB RAM · 216 MHz
$25–$60 (dev board)
STM32H7
STMicroelectronics
1024 KB RAM · 480 MHz
$30–$80 (dev board)
STM32L4
STMicroelectronics
128 KB RAM · 80 MHz
$15–$50 (dev board)
STM32U5
STMicroelectronics
786 KB RAM · 160 MHz
$20–$50 (dev board)
Hardware Guides
Arduino Nano 33 BLE Anomaly Detection Edge Impulse
The Arduino Nano 33 BLE Sense runs Edge Impulse anomaly detection using its built-in sensors — accelerometer, temperature, and microphone. E…
ESP32 Anomaly Detection with Edge Impulse
Espressif's ESP32 excels at anomaly detection via Edge Impulse. The 2-core xtensa-lx6 at 240 MHz with 520 KB SRAM handles 15 KB quantized mo…
ESP32 Anomaly Detection with TFLite Micro
The ESP32 runs autoencoder-based anomaly detection with TFLite Micro by learning normal sensor patterns and flagging deviations. Models unde…
ESP32-C3 Anomaly Detection with Edge Impulse
For anomaly detection, the ESP32-C3 with Edge Impulse scores Excellent. Its 400 KB internal SRAM (12.5x the required 32 KB) and 160 MHz cloc…
ESP32-C3 Anomaly Detection with TFLite Micro
The ESP32-C3 is a cost-effective option for Wi-Fi-connected anomaly detection. Its 400 KB SRAM runs autoencoder models comfortably while the…
ESP32-C6 Anomaly Detection with Edge Impulse
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 mod…
ESP32-C6 Anomaly Detection with TFLite Micro
For anomaly detection, the ESP32-C6 with TFLite Micro scores Excellent. Its 512 KB internal SRAM (16.0x the required 32 KB) and 160 MHz cloc…
ESP32-S3 Anomaly Detection with Edge Impulse
Espressif's ESP32-S3 excels at anomaly detection via Edge Impulse. The 2-core xtensa-lx7 at 240 MHz with 512 KB SRAM handles 15 KB quantized…
ESP32-S3 Anomaly Detection with TFLite Micro
For anomaly detection, the ESP32-S3 with TFLite Micro scores Excellent. Its 512 KB internal SRAM (16.0x the required 32 KB) and 240 MHz cloc…
i.MX RT1062 Anomaly Detection with CMSIS-NN
NXP's i.MX RT1062 excels at anomaly detection via CMSIS-NN. The 1-core cortex-m7 at 600 MHz with 1024 KB SRAM handles 15 KB quantized models…
i.MX RT1062 Anomaly Detection with TFLite Micro
For anomaly detection, the i.MX RT1062 with TFLite Micro scores Excellent. Its 1024 KB internal SRAM (32.0x the required 32 KB) and 600 MHz …
nRF52833 Anomaly Detection with Edge Impulse
Nordic Semiconductor's nRF52833 excels at anomaly detection via Edge Impulse. The 1-core cortex-m4f at 64 MHz with 128 KB SRAM handles 15 KB…
nRF52833 Anomaly Detection with TFLite Micro
Nordic Semiconductor's nRF52833 excels at anomaly detection via TFLite Micro. The 1-core cortex-m4f at 64 MHz with 128 KB SRAM handles 15 KB…
nRF52840 Anomaly Detection with Edge Impulse
For anomaly detection, the nRF52840 with Edge Impulse scores Excellent. Its 256 KB internal SRAM (8.0x the required 32 KB) and 64 MHz clock …
nRF52840 Anomaly Detection with TFLite Micro
Nordic Semiconductor's nRF52840 excels at anomaly detection via TFLite Micro. The 1-core cortex-m4f at 64 MHz with 256 KB SRAM handles 15 KB…
RA6M5 Anomaly Detection with CMSIS-NN
The RA6M5 is an excellent match for anomaly detection with CMSIS-NN. 512 KB SRAM delivers 16.0x the 32 KB minimum while 200 MHz processes 15…
RA6M5 Anomaly Detection with TFLite Micro
For anomaly detection, the RA6M5 with TFLite Micro scores Excellent. Its 512 KB internal SRAM (16.0x the required 32 KB) and 200 MHz clock e…
STM32F4 Anomaly Detection with Edge Impulse
For anomaly detection, the STM32F4 with Edge Impulse scores Excellent. Its 192 KB internal SRAM (6.0x the required 32 KB) and 168 MHz clock …
STM32F4 Anomaly Detection with TFLite Micro
The STM32F4 runs autoencoder-based anomaly detection with TFLite Micro using under 20 KB of its 192 KB SRAM. The Cortex-M4F's DSP instructio…
STM32F7 Anomaly Detection with CMSIS-NN
For anomaly detection, the STM32F7 with CMSIS-NN scores Excellent. Its 512 KB internal SRAM (16.0x the required 32 KB) and 216 MHz clock ens…
STM32F7 Anomaly Detection with TFLite Micro
For anomaly detection, the STM32F7 with TFLite Micro scores Excellent. Its 512 KB internal SRAM (16.0x the required 32 KB) and 216 MHz clock…
STM32H7 Anomaly Detection with CMSIS-NN
For anomaly detection, the STM32H7 with CMSIS-NN scores Excellent. Its 1024 KB internal SRAM (32.0x the required 32 KB) and 480 MHz clock en…
STM32H7 Anomaly Detection with TFLite Micro
The STM32H7 is an excellent match for anomaly detection with TFLite Micro. 1024 KB SRAM delivers 32.0x the 32 KB minimum while 480 MHz proce…
STM32L4 Anomaly Detection with Edge Impulse
For anomaly detection, the STM32L4 with Edge Impulse scores Excellent. Its 128 KB internal SRAM (4.0x the required 32 KB) and 80 MHz clock e…
STM32L4 Anomaly Detection with TFLite Micro
The STM32L4 pairs anomaly detection with extreme power efficiency. Its 128 KB SRAM runs autoencoder models while drawing under 100 nA in shu…
STM32U5 Anomaly Detection with CMSIS-NN
The STM32U5 is an excellent match for anomaly detection with CMSIS-NN. 786 KB SRAM delivers 24.6x the 32 KB minimum while 160 MHz processes …
STM32U5 Anomaly Detection with TFLite Micro
The STM32U5 is an excellent match for anomaly detection with TFLite Micro. 786 KB SRAM delivers 24.6x the 32 KB minimum while 160 MHz proces…
Hardware Comparisons for Anomaly Detection
ESP32-C3 vs i.MX RT1062 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 95)
ESP32-C3 vs nRF52840 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
ESP32-C3 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
ESP32-C3 vs STM32F4 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
ESP32-C3 vs STM32H7 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 95)
ESP32-C3 vs STM32L4 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
ESP32-S3 vs i.MX RT1062 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 95)
ESP32-S3 vs nRF52840 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
ESP32-S3 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
ESP32-S3 vs STM32F4 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
ESP32-S3 vs STM32H7 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 95)
ESP32-S3 vs STM32L4 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
ESP32 vs i.MX RT1062 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 95)
ESP32 vs nRF52840 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
ESP32 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
ESP32 vs STM32F4 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
ESP32 vs STM32H7 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 95)
ESP32 vs STM32L4 for Anomaly Detection
Side-by-side specs and compatibility scores (85 vs 90)
i.MX RT1062 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (95 vs 90)
nRF52840 vs i.MX RT1062 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 95)
nRF52840 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
STM32F4 vs i.MX RT1062 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 95)
STM32F4 vs nRF52840 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
STM32F4 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
STM32H7 vs i.MX RT1062 for Anomaly Detection
Side-by-side specs and compatibility scores (95 vs 95)
STM32H7 vs nRF52840 for Anomaly Detection
Side-by-side specs and compatibility scores (95 vs 90)
STM32H7 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (95 vs 90)
STM32L4 vs i.MX RT1062 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 95)
STM32L4 vs nRF52840 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
STM32L4 vs RA6M5 for Anomaly Detection
Side-by-side specs and compatibility scores (90 vs 90)
Industry Applications
Orchestrate Anomaly Detection with ForestHub
Your devices run anomaly detection on-device. ForestHub on your Linux edge gateway ingests their results over MQTT/Modbus/OPC-UA, orchestrates the sense-reason-act loop as an auditable graph, and acts on the line — the LLM is one node among many.