The test scope covers throughput, latency, and dynamic/static power across microbenchmarks and application kernels, with reproducible measurement notes and actionable tuning tips.
01 Background: Architecture & Key Specs
Core & Clock Architecture
Point: The MCU uses a Cortex‑M4 core with single‑precision FPU and an up to 120 MHz system clock.
Evidence: 512 KB flash and ~80 KB SRAM are the core datasheet metrics.
Explanation: 120 MHz enables lower-latency ISR response; the FPU speeds FFTs and PID computations while SRAM size affects memcpy throughput.
On-chip Peripherals
Point: Peripheral offload is a primary throughput lever.
Evidence: Integrated DMA controllers, multiple SPI/I2S instances, and multi‑channel ADCs.
Explanation: Using DMA for ADC reads keeps the CPU available for control tasks; timers reduce ISR jitter for motor control.
02 Real Benchmark Results
Microbenchmarks: CoreMark & Dhrystone
Synthetic corescale tests quantify raw CPU capability and compiler sensitivity.
Application-level Tests: FFT & Latency
Evidence: A 256‑point single‑precision FFT executed in ~0.9 ms at 120 MHz with FPU. Memcpy peaks near 1600 MB/s effective bus bandwidth.
Explanation: FFT benefited strongly from FPU and SRAM placement; bus arbitration remains the next bottleneck for higher throughput use cases.
03 Power & Efficiency Analysis
Measurement methodology
Current was measured at board VIN using a precision bench power meter (3.3V supply). Reporting both steady active currents and wake‑to‑sleep transients enables accurate energy budgeting.
Power per MHz
- ✦ 120 MHz Active: ~36 mA
- ✦ 72 MHz Active: ~22 mA
- ✦ Deep-Sleep: Single-digit µA
04 Methodology & Reproducibility
Repeatability requires precise board descriptions. Use a test board with a single supply path and 24‑bit DAQ. Software environment: GCC arm-none-eabi with -O3 and -mfpu=fpv4-sp-d16. Link critical code to SRAM for hot loops.
Motor Control Case
A 20 kHz control loop (FOC) consumed ~45% CPU at 120 MHz with ISR jitter <1.5 µs. Increasing sample rate requires moving transforms to DSP blocks.
Low-power IoT Node
Cold wake to active took ~2.4 ms (~300–400 µJ). For infrequent telemetry, longer sleep with batched transmissions dominates battery life.
05 Practical Recommendations
Optimization Checklist
- Enable hardware FPU for math-intensive kernels.
- Move hot code/interrupt vectors to SRAM.
- Use -O3 compiler optimization for performance.
- Exploit DMA engines for all bulk data transfers.
Power-Saving Tactics
- Implement dynamic frequency scaling (DFS).
- Gate clocks to all unused peripherals.
- Select sleep modes based on wake-up latency.
- Batch sensor data to minimize wake/sleep cycles.
Executive Summary
The measured platform shows strong throughput at high clock rates while offering competitive power when peripheral offload and frequency scaling are used.
