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Designers can add an external secure EEPROM to a microcontroller that provides an efficient way to protect sensitive data from hackers.
Using a security IC and a software library, developers can employ a more efficient approach to mutual authentication required for IoT security.
Developers should consider these low-power processor and board solutions to implement efficient machine learning on IoT devices at the network edge.
Developers can use low-cost boards and specialized LEDs to build a complete greenhouse control system.
Second generation GNSS modules offer faster satellite acquisition and better positional accuracy while becoming easier to design in.
Use highly integrated modular gate drives to save BLDC motor control circuit space and make it easier to control MOSFET switching for optimum performance.
Integrated time of flight (ToF) sensors provide a simple, fast, and inexpensive way to measure distance and to recognize simple gestures in embedded systems.
The Arduino MKR Vidor 4000 provides a simple and inexpensive way to make use of an FPGA’s real time abilities in embedded system designs.
Securing embedded systems using the Armv8-M architecture and TrustZone.
32비트 MCU 제품군을 사용하여 저전력에 최적화하는 동시에 웨어러블 및 IoT 장치에 보이지 않는 인텔리전스를 구축합니다.
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.
고급 MCU 및 PMIC는 차세대 스마트 제품에 대한 사용자의 기대치를 충족하는 데 필요한 토대를 제공합니다.
Developers can add voice-activated features to resource-constrained designs using a low-power FPGA running a BNN, a highly efficient machine learning algorithm.
최소 설계 시간, 부품 수, 전력 소비를 사용하는 내장형 시스템에 실시간 클록/캘린더( RTCC) 기능을 구현합니다.
Zigbee Light Link eases development of wireless mesh connected, low-power smart lighting.
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.
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