STM32F407 Microcontroller Series

• STM32F407 series microcontrollers are integrated systems built around an ARM Cortex-M4F core with fixed-point DSP capabilities, enabling high-efficiency embedded AI inference.
• They optimize performance for quantized neural network inference by employing symmetric quantization and specialized MAC instructions that reduce computational overhead.
• Robust memory architecture and advanced power management, including multiple SRAM types and low-power modes, ensure real-time processing and energy efficiency for edge applications.

The STM32F407 series microcontroller is centered on the ARM Cortex-M4F core with a single-precision floating-point unit (FPU), operating at frequencies up to 168 MHz. This platform balances computation, memory, and energy efficiency for embedded AI, signal processing, and control workloads. Notable for its deterministic memory hierarchy, fixed-point arithmetic support, and specialized DSP instructions, the STM32F407 series is widely employed as a hardware target for quantized deep neural network inference, particularly in constrained edge scenarios (Novac et al., 2021).

1. System Architecture and AI Inference Capabilities

The STM32F407VG and its derivatives feature an ARM Cortex-M4F CPU, 1 MiB on-chip flash, and a 192 KiB SRAM subsystem partitioned into 112 KiB main SRAM (AXI-AHB accessible), 64 KiB Core Coupled Memory (CCM, zero-wait-state), and 16 KiB backup SRAM. The single-precision FPU (IEEE-754) yields one-cycle throughput for add/multiply (two cycles pipeline latency), but lacks hardware support for half-precision or double-precision. Software emulation of double-precision incurs significant penalty (tens of cycles per operation).

Memory access patterns are critical: flash incurs up to six wait states at max frequency, with a read bandwidth of approximately 16 MB/s per AHB master. CCM is suitable for hot buffers and intermediate feature maps due to its zero-wait-state characteristics. The architecture includes separate AHB buses for code (I-code, flash) and data (D-code, SRAM/Periph). The absence of NEON SIMD restricts vector computation, but the DSP extension (SMLAD and related instructions) enables two 16×16 MAC operations per cycle.

For AI inference, fixed-point computations can surpass floating-point in performance by exploiting absence of FPU stalls and flash wait-states, provided tight memory placement and prefetching techniques are applied. Code and constant weights should be placed in flash, while large performance-critical feature maps should utilize CCM (Novac et al., 2021).

2. Quantization and Fixed-Point Representation

Deployment to STM32F407 increasingly relies on quantization of trained floating-point networks to fixed-point formats (int8 or int16) using Q-format arithmetic. Uniform symmetric quantization with power-of-two scale factors is preferable, reducing multiplication to shift operations.

Given a tensor $X = [x_1, \dots, x_N]$, the Q-format $(m, n)$ is determined by

$$m = 1 + \lfloor \log_2 \max_i |x_i| \rfloor,\quad n = w - m - 1$$

for a $w$-bit word. The scale factor is $S = 2^{-n}$ and quantization proceeds as:

$$q_i = \textrm{sat}\bigl( \mathrm{round}(x_i \times 2^n) \bigr),\quad x_i \approx q_i \times S$$

where $\mathrm{sat}(\cdot)$ clamps the value to the signed integer range.

MAC operations between $Qm_1.n_1$ activations and $Qm_2.n_2$ weights result in $Q(m_1+m_2).(n_1+n_2)$, with accumulators sized at $2w$ bits. Shifts and saturation are applied after accumulation to retrieve results in the output format. Per-layer quantization is standard, minimizing computational overhead, though per-filter quantization can yield lower distortion at the cost of additional complexity (Novac et al., 2021).

3. Memory Footprint and Buffer Allocation

Flash memory requirements for weights are computed as:

$$\text{Flash}_\text{weights} = \sum_{l=1}^L \left( \#\mathrm{params}_l \times \frac{b}{8} \right)$$

where $b$ is the bit width (8 or 16). For activations and temporaries, the primary RAM requirement is set by the largest activation per layer and buffer requirements for residual connections:

$$\text{RAM}_\text{acts} \approx \max_l(A_l) \times \frac{b}{8} + \sum_{\text{temporaries}}$$

Buffer reuse is managed by a top-level allocator that exploits non-overlapping lifetimes (as in MicroAI). Typical runtime and operator code (CMSIS-NN, MicroAI) occupy 20–30 KiB of flash (Novac et al., 2021).

Resource Type	Typical Use in STM32F407	Example Value (int8)
Flash (weights)	Model weights & code	240–480 B per conv
SRAM (activations)	Buffers, feature maps	2 KiB per layer
Flash (code)	Operators, runtime, allocator	20–30 KiB

4. Real-Time Performance and Cycle Analysis

The real-time predictability of inference is established by explicit MACC counts:

$$\text{MACCs} = C_{\text{in}} \times C_{\text{out}} \times K \times S_{\text{out}}$$

For CMSIS-NN SMLAD-based convolutions, each MACC maps to one cycle. Memory transactions (loads/stores) add 1–2 cycles each, with further overhead for shift and saturate operations (1 + 2 cycles). The overall cycle count per layer can be approximated as:

$$\mathrm{cycles}_l \approx \mathrm{MACCs}_l \times (1 + \lambda_{\text{mem}}) + O_{\text{overhead}}$$

with $\lambda_{\text{mem}} \approx 0.2$ cycles per MACC.

Cycle-accurate profiling is performed using on-chip Data Watchpoint and Trace (DWT) counters. Pseudocode and inline intrinsics such as __SMLAD (two 16×16 MACCs per cycle) and __SSAT (saturation, one cycle) are central to achieving high efficiency. Direct measurement involves reading DWT->CYCCNT before and after kernel launches and converting to time by dividing by clock frequency (Novac et al., 2021).

5. Power Consumption and Low-Power Techniques

Operational power (at 3.3 V, 84 MHz, CMSIS-NN int8 inference on ResNet-1D6-48) is approximately 45 mA ($\approx 148$ mW). Dynamic energy management includes disabling unused peripherals (via RCC clocks), and exploiting core sleep (__WFI() for ~200 µA) or stop modes (RTC wakeup, ~2 µA, although flash is unavailable).

Dynamic frequency scaling can switch the core clock between high-speed internal (16 MHz) or lower PLL multipliers when throughput is not required. A recommended operational cycle is: reconfigure to 168 MHz for inference, execute (150–300 ms), then enter a sleep or stop state. For a 1s periodic inference, average power can be reduced to approximately 26 mW by amortizing high-power run phases with deep sleep intervals (Novac et al., 2021).

6. AI Deployment Toolchains: STM32Cube.AI and MicroAI

Two primary toolchains for deep neural network deployment are prevalent. STM32Cube.AI is a proprietary pipeline integrating with STM32CubeMX, supporting float32 and int8 post-training quantization with per-tensor and per-channel scales (asymmetric offsets), but not int16. It produces model C sources and offers automated evaluation hooks, but relies on a closed-source engine.

MicroAI is an open framework implemented in Python, converting Keras or PyTorch models via a code generator (KerasCNN2C) to C sources with explicit Q-format configuration. It outputs header/source files for layers, Q-format macros, and feature map allocators, supporting float32, int8, int16, and mixed Q-formats. The runtime is open and fully unrolled per layer for maximum flexibility and extensibility.

Recommended project organization under STM32CubeIDE includes segregation of AI-generated sources, core startup/runtime, drivers, and AI operator code. Build integration requires enabling CMSIS-NN via preprocessor macros and explicit inclusion of AI component files. Benchmarking cycles with DWT is standard. Integration is also directly supported in Keil MDK5 with minor path adjustments (Novac et al., 2021).