Note: M1W AI+lOT Module K210 Deep learning (Aerial) is
discontinued now.
AI chip normally refers to ASIC chip that aims at AI algorithms. Although the conventional CPU and GPU can be used to execute AI algorithms, they have been greatly limited in their speed, performance, and practicality. Compared with traditional processor chip, AI chip offers faster speed, more computing power, and low energy consumption.
This product employs AI chip K210 as the core unit. K210 comes with dual-core processors with independent FPU, 64 bits CPU bit width, 8MB on-chip SRAM, 400M adjustable nominal frequency, and double precision FPU supporting multiplication, division, and square root operation.
The AI Module-M1 is equipped with neural network hardware accelerator KPU, voice processing unit (APU), programmable IO array (FPIOA/IOMUX) and Fast Fourier Transform Accelerator. In the AI processing, K210 can perform operations such as convolution, batch normalization, activation, and pooling. At the same time, the pre-processing of voice direction scanning and voice data output can also be performed.
M1W module has WIFI chip module (include antenna) based on M1 chip.
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CPU: RISC-V dual-core 64 bit
400Mhz overclockable
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Debugging Support: high-speed UART and JTAG interface for debugging
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Neural Network Processor: each layer of convolutional neural network parameter can be configured separately, including the number of input and output channels, and the input and output line width and column height.
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support for 1×1 and 3×3 convolution kernels
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Image Recognition: QVGA@60FPS/VGA@30FPS
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Audio Processor: support up to 8 channels of audio input data, ie 4 stereo channels
16 bit wide internal audio signal processing
support for 12-bit, 16-bit, 24-bit and 32-bit input data widths
Up to 192KHz sample rate
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Static Random-Access Memory (SRAM): the SRAM is split into two parts, 6MiB of on-chip general-purpose SRAM memory and 2 MiB of on-chip AI SRAM memory
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Field Programmable IO Array: FPIOA allows users to map 255 internal functions to 48 free IOs on the chip
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Digital Video Port: maximum frame size 640x480
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FFT Accelerator: the FFT accelerator is a hardware implementation of the Fast Fourier Transform(FFT)
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Deep Learning Frame: TensorFlow/Keras/Darknet
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Peripherals: FPIOA, UART, GPIO, SPI, I2C, I2S, WDT, TIMER, RTC etc.
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WiFi: support 2.4G 802.11.b/g/n
