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== Overview == [[文件:K210-blockdiagram.jpg | 600px]] * 64-bit RISC-V CPU Dual-Core, up to 400MHz * IMAFDC (RV64GC) ISA extension * Hardware FPU (Float Point Unit), Double Precision * 32 KB I-Cache per core * 32 KB D-Cache per core * 8MB On-Chip SRAM * 128Kbit One-Time Programmable Memory (OTP) * Read Only Memory (ROM) * DMA * Neural Network Accelerator (KPU) * Audio Accelerator (APU) * FFT Accelerator * SHA256 Accelerator * AES Accelerator * DVP x1 * UART x4 * GPIO x40 * I2C x3 * SPI x4 * I2S x3 * PWM x1 * Timer x3 * RTC x1 * Watchdog Timer (WDT) x1 * JTAG, OpenOCD support * BGA144 with 12 balls on each side * The chip size is 8 x 8 x 0.953mm <br><br> == Datasheet == * [https://s3.cn-north-1.amazonaws.com.cn/dl.kendryte.com/documents/kendryte_datasheet_20180919020633.pdf K210 Datasheet CN] * [https://s3.cn-north-1.amazonaws.com.cn/dl.kendryte.com/documents/kendryte_datasheet_20181011163248_en.pdf K210 Datasheet EN] <br> == KPU == KPU is a general-purpose neural network processor with built-in convolution, batch normalization, activation, and pooling operations. It can detect faces or objects in real time. * Supports the fixed-point model that the mainstream training framework trains according to specific restriction rules * There is no direct limit on the number of network layers, and each layer of convolutional neural network parameters can be configured separately, including the number of input and output channels, and the input and output line width and column height * Support for 1x1 and 3x3 convolution kernels * Support for any form of activation function * The maximum supported neural network parameter size for real-time work is 5MiB to 5.9MiB * The maximum supported network parameter size when working in non-real time is (flash size - software size) <br> == APU == <br> == Reference == * https://kendryte.com * https://kendryte.com/downloads/ * https://github.com/kendryte <br><br> <br><br> <br><br> <br><br> <br><br> <br><br> <br><br> <br><br> <br><br>
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