Hardware Guide
The ESP32 handles people counting effectively with TFLite Micro. 520 KB SRAM at 240 MHz provides 2.7x headroom over the 192 KB requirement for 200 KB models. Built-in Wi-Fi 802.11 b/g/n enables wireless result reporting.
| Spec | ESP32 |
|---|---|
| Processor | Dual-core Xtensa LX6 @ 240 MHz |
| SRAM | 520 KB |
| Flash | Up to 16 MB (external) |
| Key Features | Hardware crypto acceleration, Ultra-low-power co-processor (ULP) |
| Connectivity | Wi-Fi 802.11 b/g/n, Bluetooth 4.2 BR/EDR + BLE |
| Price Range | $2 - $5 (chip), $5 - $15 (dev board) |
Memory-wise, the ESP32 offers 520 KB SRAM, which delivers 2.7x the 192 KB minimum needed for people counting. The 200 KB quantized model fits in the tensor arena with enough remaining capacity for input buffers and core application logic. More demanding features (multi-sensor fusion, large protocol stacks) may require careful allocation planning. Flash storage at 16 MB comfortably houses the TFLite Micro runtime, the 200 KB model binary, application firmware, and OTA update partitions for field upgrades. Flash usage is well within budget for this configuration. The ESP32's dual-core Xtensa LX6 allows dedicating one core to inference while the other handles Wi-Fi/BLE communication and application logic. The ULP co-processor can handle simple sensor reads during deep sleep, reducing average power consumption in duty-cycled deployments. People Counting requires camera input. The ESP32 lacks native peripheral support for some of these sensors, requiring external interface circuitry. A camera interface (DVP/DCMI) is not available — SPI-based camera modules may work but with reduced frame rates. Evaluate whether the peripheral gap justifies an alternative MCU with native support. TFLite Micro's static memory allocation model maps well to the ESP32's memory architecture — define a fixed tensor arena at compile time with no runtime heap fragmentation risk. The framework's operator coverage supports convolutional, depthwise-separable, and pooling layers needed for people counting. Model conversion uses the standard TFLite converter with int8 post-training quantization. At $2-5 per chip ($5-15 for dev boards), the ESP32 is a reasonable investment for people counting deployments. With 136 PlatformIO-listed boards, hardware availability is excellent. Key ESP32 features for this workload: Hardware crypto acceleration, Ultra-low-power co-processor (ULP).
Set up ESP32 development environment
Install ESP-IDF (recommended for production) or Arduino framework via PlatformIO. Create a project targeting the ESP32 and verify basic functionality (blink LED, serial output). For TFLite Micro, clone the framework repository and add it as a library dependency. Ensure the toolchain supports C++11 or later for the ML runtime.
Collect camera training data
Connect a camera module (e.g., OV2640 via DVP/SPI) to the ESP32. Write a data logging sketch that captures camera readings at the target sample rate and outputs via serial/SD card. Collect 1000+ labeled samples across all classes. Capture images at the model input resolution (96×96 or lower).
Train and quantize model for TFLite Micro
Build a quantized MobileNet-SSD or YOLO-Tiny in TensorFlow or PyTorch. Apply int8 post-training quantization — this typically reduces model size by 4x with minimal accuracy loss. Convert to .tflite and generate a C array (xxd -i model.tflite > model_data.h). Target model size: under 200 KB to fit the ESP32's 520 KB SRAM with room for application code.
Deploy and validate on ESP32
Include the TFLite Micro runtime and compiled model in your Espressif project. Allocate a tensor arena of 300-500 KB in a static buffer. Run inference on live camera 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.
STMicroelectronics cortex-m7 at 480 MHz with 1024 KB SRAM. $8-20 per chip. Compared to ESP32: more RAM, faster clock. Excellent rated.
Espressif xtensa-lx7 at 240 MHz with 512 KB SRAM. $3-8 per chip. Excellent rated.
NXP cortex-m7 at 600 MHz with 1024 KB SRAM. $6-12 per chip. Compared to ESP32: more RAM, faster clock. Excellent rated.
Connect cameras to on-device inference — design detection workflows visually and compile to optimized firmware.
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