SiLabs debuts Wonder Gecko MCU sensor kits -

SiLabs debuts Wonder Gecko MCU sensor kits

With its acquisition of Energy Micro now complete, Silicon Labs is moving into the market with a tool kit for building DSP-optimized smart sensor systems with the acquired company’s Wonder Gecko MCUs.

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.

According to Geir Førre, senior vice president and general manager of Silicon Labs’ microcontroller business, the development kits and included software examples are designed to help embedded engineers leverage 32-bit digital signal control with the high-performance CPU and extremely low standby power modes.

Key to the usefulness of the sensor development kit, he said, is the ease with which developers will be able to access the Wonder Gecko’s 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,” he said, “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. Software examples in the kit include:

1 – 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)

2 – 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

3 – An application example using the kit’s onboard light sensor for 10-500 Hz FFT analysis.

The reason the software demonstrations have been included, said Førre, was to make it easier for 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 ARM Cortex-M processors.

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