BT817Q-R

The BT817Q-R from Bridgetek Pte Ltd. revolutionizes Embedded - Microcontrollers - Application Specific by delivering unmatched computational density for AI edge applications. This application-specific microcontroller combines neural network acceleration with traditional control capabilities, enabling intelligent embedded systems.Engineered for Human Machine Interfaces (HMI) scenarios, the device includes hardware accelerators for TensorFlow Lite operations. The architecture supports concurrent execution of control algorithms and machine learning inference.With FLASH flexibility, the memory subsystem supports model parameter storage. The hierarchical cache architecture reduces external memory bandwidth requirements by 60%.As part of the BT81X family, this microcontroller shares AI development tools with companion devices. The unified software stack simplifies migration between performance tiers.The LED, LCD, QSPI, SPI connectivity includes high-speed MIPI CSI-2 for vision sensors. Each interface implements adaptive equalization for reliable data transmission over long cables.Featuring 4 programmable interfaces, the device supports parallel sensor data acquisition. The I/O banks maintain signal integrity at 200MHz clock rates for vision processing.Operating from 3V ~ 3.6V inputs, the power management unit supports dynamic frequency scaling. The integrated LDOs achieve 70dB power supply rejection for noise-sensitive analog circuits.Rated for -40°C ~ 85°C (TA) operation, the package incorporates thermal monitoring diodes. The advanced packaging technology maintains junction temperatures below critical levels during sustained compute loads.The Surface Mount configuration supports high-density PCB layouts common in edge devices. The package footprint minimizes board space while maintaining adequate thermal dissipation.Available in 64-VFQFN Exposed Pad format, the package includes EMI shielding for RF-sensitive applications. The low-inductance package design enables clean power delivery at high clock frequencies.Delivered in 64-VQFN (9x9) configuration, each unit includes pre-programmed neural network kernels. The production flow includes 100% functional testing of AI acceleration features.