Hardware Guide
STMicroelectronics's STM32F7 excels at image classification via CMSIS-NN. The 1-core cortex-m7 at 216 MHz with 512 KB SRAM handles 150 KB quantized models with 4.0x RAM headroom. Built-in Ethernet enables wireless result reporting.
| Spec | STM32F7 |
|---|---|
| Processor | ARM Cortex-M7 @ 216 MHz |
| SRAM | 512 KB |
| Flash | 2 MB |
| Key Features | Double-precision FPU, L1 cache (16 KB I + 16 KB D), ART Accelerator, Chrom-ART (DMA2D), TFT-LCD controller |
| Connectivity | Ethernet, USB OTG HS/FS |
| Price Range | $8 - $15 (chip), $25 - $60 (dev board) |
With 512 KB of internal SRAM, the STM32F7 provides 4.0x the 128 KB minimum for image classification. This generous headroom means the 150 KB model tensor arena, sensor input buffers, and application logic (camera polling, Ethernet stack, state management) all fit without contention. The remaining 137 KB after model allocation supports complex application features. The STM32F7 provides 2 MB of flash memory, which comfortably houses the CMSIS-NN runtime, the 150 KB model binary, application firmware, and OTA update partitions for field upgrades. Flash usage is well within budget for this configuration. The STM32F7 at 216 MHz with Cortex-M7 instruction and data caches delivers near-real-time inference for mid-size models. Its 512 KB SRAM handles most sensor and audio ML workloads. The ART accelerator reduces flash access latency during inference. For image classification, connect a camera module (e.g., OV2640 via DVP/SPI) via SPI to the STM32F7. The camera interface supports QVGA (320×240) or lower resolution for on-device inference. Downsample to the model's input size (typically 48×48 to 96×96 pixels) before feeding the neural network. CMSIS-NN provides ARM-optimized neural network kernels that leverage the STM32F7's DSP instructions and floating-point unit for maximum inference throughput on Cortex-M. The kernels are hand-optimized in assembly for critical operations (Conv2D, DepthwiseConv2D, FullyConnected). Combine with TFLite Micro's CMSIS-NN delegate for the best performance on ARM targets. At $8-15 per chip ($25-60 for dev boards), the STM32F7 offers strong value for image classification deployments. Key STM32F7 features for this workload: Double-precision FPU, L1 cache (16 KB I + 16 KB D), ART Accelerator, Chrom-ART (DMA2D), TFT-LCD controller.
Set up STM32F7 development environment
Install STM32CubeIDE with the latest STM32Cube firmware package. Create a project targeting the STM32F7 and verify basic functionality (blink LED, serial output). For CMSIS-NN, 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 STM32F7. 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 model and prepare for CMSIS-NN deployment
Train a quantized MobileNetV2 or EfficientNet-Lite in TensorFlow/Keras. Apply int8 post-training quantization via the TFLite converter — this is essential for CMSIS-NN's optimized kernels. The quantized model should be under 150 KB. Use tflite_micro's CMSIS-NN delegate to automatically route operations to optimized ARM kernels on the STM32F7's cortex-m7 core.
Deploy and validate on STM32F7
Include the CMSIS-NN runtime and compiled model in your STMicroelectronics project. Allocate a tensor arena of 225-375 KB in a static buffer. Run inference on live camera data and compare predictions against your test set. Log results to serial for desktop 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 STM32F7: more RAM, faster clock. Excellent rated.
NXP cortex-m7 at 600 MHz with 1024 KB SRAM. $6-12 per chip. Compared to STM32F7: more RAM, faster clock, cheaper. Excellent rated.
Renesas cortex-m33 at 200 MHz with 512 KB SRAM. $6-12 per chip. Compared to STM32F7: cheaper. Good rated.
Design classification pipelines from camera input to edge inference — compile to firmware with ForestHub's visual workflow builder.
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