CY8CTST200-32LQXI

The CY8CTST200-32LQXI from Rochester Electronics, LLC 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 Touchscreen Controller scenarios, the device includes hardware accelerators for TensorFlow Lite operations. The architecture supports concurrent execution of control algorithms and machine learning inference.At its core, the M8C delivers 4x the vector processing performance of previous generations. The SIMD (Single Instruction Multiple Data) units accelerate matrix operations essential for neural networks.With FLASH (32kB) flexibility, the memory subsystem supports model parameter storage. The hierarchical cache architecture reduces external memory bandwidth requirements by 60%.As part of the CY8CT family, this microcontroller shares AI development tools with companion devices. The unified software stack simplifies migration between performance tiers.The 2K x 8 memory architecture supports weight streaming for large neural networks. The memory controller implements bank interleaving to maximize bandwidth utilization.The I²C, SPI, UART/USART, USB connectivity includes high-speed MIPI CSI-2 for vision sensors. Each interface implements adaptive equalization for reliable data transmission over long cables.Featuring 28 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 1.8V 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 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 32-UFQFN 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 32-QFN (5x5) configuration, each unit includes pre-programmed neural network kernels. The production flow includes 100% functional testing of AI acceleration features.