The Wonder Gecko MCU line is based on the ARM Cortex-M4 processor core, which provides a full DSP instruction set and includes a hardware floating point unit (FPU) for faster computation performance. The development kits and software examples are designed to help embedded engineers leverage 32-bit digital signal control with the high-performance CPU and low standby modes.
"With our focus on energy efficiency, the Wonder Gecko kits give embedded designers access to the most energy-friendly ARM Cortex-M4 and the lowest standby power modes," said Geir Førre, senior vice president and general manager of Silicon Labs’ microcontroller business. "The Wonder Gecko development kits and software library provides easy access to advanced signal processing functions and floating point performance. More and more instances of smart sensor and wireless applications benefit from effective analysis locally at the sensor node rather than transmitting large volumes of data over the network for remote processing."
To speed up the design time, the EFM32 development kits include a built-in J-Link debugger and come with software examples using each kit’s built-in features such as an audio pre-amplifier equalizer that digitizes the audio connector signal with the MCU’s on-chip analog-to-digital converter (ADC) and subsequently generates the output via a digital-to-analog converter (DAC).
An audio frequency analyzer using the kit’s audio connector and performing a Fast Fourier Transform (FFT) to display a frequency plot on the development kit’s LCD. There is also an application example using the kit’s onboard light sensor for 10-500 Hz FFT analysis.
The software demonstrations also enable designers to evaluate the differences between hard and soft floating-point operations and compiler optimization, as well as the CPU cycle count.
The example projects are coded using algorithms that are part of the Cortex Microcontroller Software Interface Standard (CMSIS) DSP function library, which includes complex FFT, finite impulse response (FIR) filters, matrix and vector operations, and statistical analysis. CMSIS provides a vendor-independent hardware abstraction layer for Cortex-M