The evaluation kit for the AIROC™ CYW55913 Wi-Fi® and Bluetooth® Low Energy (LE) connected MCU enables evaluation, prototyping, and development of CYW55913, in addition to the CYW55912 and CYW55911, for a wide array of applications for the Internet of Things.
The PSOC Edge E84 evaluation kit is a hardware evaluation platform for rapid prototyping of applications based on the PSOC Edge MCU family. Featuring the high-performance, low-power, secured MCUs with advanced machine learning (ML) acceleration for next-generation AI/ML applications, this evaluation kit provides easy access to all of the interfaces and capabilities of the PSOC Edge MCUs.
The OPTIGA™ TPM SLB9673 RPI evaluation board provides a quick and easy way to start to develop with the Infineon SLB9673 trusted platform module (TPM) for Raspberry Pi. The board comes as a Raspberry Pi hardware attached on top (HAT) that conforms with the rules defined by the Raspberry Pi Foundation. This add-on makes it easier for users to connect the board to all 40-pin GPIO Raspberry Pi boards
The OPTIGA™ Authenticate NBT Development Shield combines hardware components to allow evaluation and development of applications for the OPTIGA™ Authenticate NBT, NFC Type 4 asymmetric tag.
For activating and using shared e-bikes with NFC-enabled smart phones, locking and unlocking personal portable HDD drives with NFC-enabled smart phones, and healthcare applications involving data logging and secured data monitoring. The industrial applications involve configuration and parametrization of electric relay switches, circuit breakers and more.
AI evaluation kit targets inference and always-on workloads on PSOC™ Edge E84
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.
Evaluation board supports AI and HMI development on PSOC Edge E84
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.
