The PSOC Edge E84 MCU further extends the broad PSOC portfolio of microcontrollers, which features hardware-assisted machine learning (ML) acceleration for use in next-generation responsive compute and control applications.
The PSOC Edge MCUs use the capability of AI to improve end-user experiences by enabling end products to be more intelligent and intuitively usable, lowering the barriers to human-machine interaction and adding contextual awareness. They do so while providing robust privacy and safety protection through embedded Infineon Edge Protect technologies.
The PSOC Edge E84 is based on a power-optimized multi-core architecture. A high-performance block features an Arm® Cortex®-M55 CPU core, including Helium DSP support, paired with an Arm Ethos™-U55 neural processing unit (NPU). A low-power block combines a Cortex-M33 CPU paired with the ultra-low-power Infineon NNLite hardware accelerator for neural networks in always-on ML and AI applications. PSOC Edge family MCUs feature advanced memory resources, with up to 6 Mbytes of system SRAM and 512 kbytes of non-volatile RRAM memory shared across the high-performance and low-power blocks.
The MCU provides an always-on acoustic activity detection capability which enables the operation of a human-machine interface (HMI) with low active and standby power consumption. It also includes support for advanced graphics, voice, and audio, as well as standard communication and timing peripherals suitable for a variety of consumer and industrial applications.
Software enablement is based on the ModusToolbox™ development software suite, including integration with the DEEPCRAFT™ AI Suite solution with off-the shelf DEEPCRAFT™ Ready Models. ModusToolbox is a comprehensive collection of multi-platform tools and software libraries which includes board support packages, a peripheral driver library, and middleware libraries.
KIT_PSE84_EVAL
The KIT_PSE84_EVAL evaluation board provides a hardware platform for assessing the capabilities of the Infineon PSOC Edge E84 microcontroller in AI-enabled embedded systems. The board exposes both the high-performance and low-power compute domains, allowing evaluation of workloads that combine neural network inference, signal processing, and real-time control.
The evaluation board supports development of always-on AI use cases by enabling interaction between the Cortex®-M55 with Ethos™-U55 NPU and the low-power Cortex-M33 with NNLite accelerator. Memory resources are accessible to demonstrate shared SRAM and non-volatile RRAM usage across compute domains. Interfaces for audio, display, sensors, and communication peripherals allow investigation of voice, graphics, and HMI workloads under representative system conditions.
Debug and programming interfaces support firmware development and analysis using the ModusToolbox™ software environment, with compatibility for AI workflows using DEEPCRAFT™ Studio. The board is intended for evaluation of edge AI, low-power inference, and multi-core task partitioning in consumer, industrial, and security-focused applications.
KIT_PSE84_AI
The KIT_PSE84_AI evaluation kit is intended for assessment of artificial intelligence and machine learning workloads on the Infineon PSOC™ Edge E84 microcontroller. The board is configured to demonstrate the interaction between the high-performance compute domain, based on the Arm® Cortex®-M55 with Ethos™-U55 NPU, and the low-power always-on domain, which combines a Cortex-M33 core with Infineon’s NNLite neural network accelerator.
The platform supports evaluation of common edge AI use cases such as acoustic activity detection, voice processing, and sensor-based inference. Audio interfaces, sensor connectivity, including 60GHz radar and pressure sensors as well as memory access are provided to allow execution of neural network models across shared SRAM and non-volatile RRAM resources. This enables analysis of task partitioning, power-domain interaction, and inference latency under representative operating conditions.
The kit is supported by the ModusToolbox™ development environment and integrates with DEEPCRAFT™ Studio tooling for model deployment and validation. It is intended for engineers evaluating AI-enabled embedded architectures where low-power, always-on inference and higher-performance processing must operate within a single device.
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