Developers can use secure MCUs and authentication ICs to meet users' expectations for privacy and protection in smart products.
Developers can implement efficient KWS on MCUs using DS-CNNs from hardware optimized neural network library for resource-constrained designs.
Advanced MCUs and PMICs provide the foundation needed to meet user expectations for next generation smart products.
Use redrivers to expand high-speed USB applications by enabling the use of longer cables.
Current sense resistors are widely used to measure current flow to track system performance, but there are nuances to be aware of for successful implementation.
Developers can easily switch audio and video sources while minimizing noise and signal losses by using the right CMOS analog switches.
IMUs provide precise positioning information for embedded applications ranging from consumer to industrial, medical, and military.
Developers can add voice-activated features to resource-constrained designs using a low-power FPGA running a BNN, a highly efficient machine learning algorithm.
After choosing between the various wireless cellular interfaces for IoT devices, designers can get LTE-M based designs to market quickly with modules and kits.
Using RMS-to-DC converters to measure the power of complex waveforms.
Implement real-time clock/calendar (RTCC) functions in embedded systems with minimal design time, component count, and power consumption.
Zigbee Light Link eases development of wireless mesh connected, low-power smart lighting.
광발전 DC 작동 전압을 높이기 위해 더 큰 셀 스트링을 사용하면 I2R 손실이 줄고 배포 비용이 절감될 수 있습니다.
Mechanical buttons and switches come in many forms to help designers meet various safety, security, ease of access, and power-up configuration requirements.
광 다이오드와 광 트랜지스터는 다양한 응용 분야의 핵심 요소이지만, 전기, 광학 및 기계 측면에서 특수하게 고려해야 할 사항이 있습니다.
Developers can use machine learning in embedded vision applications using off-the-shelf FPGAs now available on specialized platforms.
Using stable hardware and software like the Renesas Synergy Platform, see how to get an IoT application connected to the cloud in 10 minutes.
Using the Raspberry Pi 3 and compatible versions of machine learning software, developers can begin developing sophisticated machine learning applications.
Machine learning development is going mainstream thanks to accessible software and methods that enable deployment on readily available processors and FPGAs.
Development kits enable engineers to turn air quality sensors into Internet of Things connected devices.
