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Silicon Labs Speeds DSP Smart Sensor System Design With Wonder Gecko MCU Development Kits

Silicon Labs (Nasdaq: SLAB), a leader in high-performance, analog-intensive, mixed-signal ICs, today introduced development kits and application software demonstrations for the EFM32 Wonder Gecko microcontroller (MCU) family, which was developed by Energy Micro, recently acquired by Silicon Labs. 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 extremely low standby power modes.

“With our focus on energy efficiency, the Wonder Gecko kits give embedded designers access to the most energy-friendly ARM Cortex-M4 based MCU 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 provide 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:

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

These 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.

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