NXP Semiconductors today announced market availability of the i.MX RT600
crossover microcontroller (MCU) family, an ideal solution for ultra-low power,
secure edge applications including audio, voice and machine learning.
The i.MX RT600 crossover MCU provides optimal balance in power, performance,
and memory. Features include:
- Arm® Cortex®-M33 running up to 300 MHz.
Optional Cadence® Tensilica® HiFi 4 audio/voice digital signal processor
(DSP) running up to 600 MHz with Quad 32x32 MAC.
Up to 4.5 MB on-chip SRAM with zero wait-state access for critical code and
- 28nm FD-SOI process optimized for both active and leakage power.
- NXP's advanced embedded security technology - EdgeLock™ 400A.
Optimized machine learning support using
Glow neural network compiler.
Product Availability and Support
The i.MXRT600 MCU family is available with a suggested resale price starting at$4.50 (USD) for
NXP will accompany the silicon release with an i.MX RT600 evaluation kit at a
suggested resale price of $129 (USD).
i.MX RT600 is fully supported by the NXP MCUXpresso suite of tools and
software, including IDE support, system configuration and extensive driver and
middleware support. In addition, Cadence's Xplorer IDE for i.MX RT600, DSP
function libraries and audio codecs are offered complementary. We've also
partnered with Alango Technologies, DSP Concepts and Sensory to provide high
performance voice pre-processing and recognition software, plus professional
audio libraries and tools. Furthermore, the integrated HiFi 4 DSP will be
supported with the NXP eIQ for Glow machine learning compiler, accessible in the next MCUXpresso SDK release planned for Q2 2020.
For more information, please visit
i.MX RT600 Crossover MCU.
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About the i.MX RT Series of Crossover MCUs
The i.MX RT series of crossover MCUs are designed to bridge the gap between
high-performance and integration while minimizing costs to meet today's need
for high performance embedded processing at the edge. The series delivers
advanced microcontrollers (MCUs) with the functionality of applications
processors, at low costs, enabling advanced computation and machine learning
capabilities in millions of connected edge devices.