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.
Using larger strings of cells to raise photovoltaic dc operating voltages can reduce I2R losses and save deployment costs.
Mechanical buttons and switches come in many forms to help designers meet various safety, security, ease of access, and power-up configuration requirements.
Photodiodes and phototransistors are key to many applications, but they require special electrical, optical, and mechanical considerations.
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.
Use isolation amplifiers to measure signals with high voltage offsets and reduce ground loops when working with multiple sensors.