DINGCEN INTERNATIONAL (HK) LIMITED
| Description | ARM ® Cortex ®- M4- Microcontroller IC 32-bit dual core 100MHz 512KB (512K x 8) Flash 81-CTBGA (8x8) |
| Stock | 10000pcs |
| Payment Terms | L/C, D/A, D/P, T/T, Western Union, MoneyGram |
| Shipping Method | Express, FCL, AIR, LCL |
| Model | MAX78000EXG+ |
| Type | Logic ICs,Drive IC,Other,NA,Voltage Regulator |
| D/c | Newest,New DC,new year |
| Condition | Original 100%,New&original,New and Original |
| Warranty | 3 year |
| Lead time | In Stock,1~3 Days |
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Product Specification
| Description | ARM ® Cortex ®- M4- Microcontroller IC 32-bit dual core 100MHz 512KB (512K x 8) Flash 81-CTBGA (8x8) | Stock | 10000pcs |
| Payment Terms | L/C, D/A, D/P, T/T, Western Union, MoneyGram | Shipping Method | Express, FCL, AIR, LCL |
| Model | MAX78000EXG+ | Type | Logic ICs,Drive IC,Other,NA,Voltage Regulator |
| D/c | Newest,New DC,new year | Condition | Original 100%,New&original,New and Original |
| Warranty | 3 year | Lead time | In Stock,1~3 Days |
MAX78000EXG+
General Description
Artificial intelligence (AI) requires extreme computationalhorsepower, but Maxim is cutting the power cord from
AI insights. The MAX78000 is a new breed of AI microcontroller built to enable neural networks to execute at ultra-low power and live at the edge of the IoT.
Thisproduct combines the most energy-efficient AI processing with Maxim's proven ultra-low power microcontrollers. Our
hardware-based convolutional neural network (CNN) accelerator enables battery-powered applications to execute AI inferences while spending only microjoules of energy.
The MAX78000 is an advanced system-on-chip featuring an Arm Cortex-M4 with FPU CPU for efficient system control with an ultra-low-power deep neural network accelerator. The CNN engine has a weight storage memory of 442KB, and can support 1-, 2-, 4-, and 8-bit weights (supporting networks of up to 3.5 million weights).
The CNN weight memory is SRAM-based, so AI network updates can be made on the fly. The CNN engine also has 512KB of data memory. The CNN architecture is highly flexible, allowing networks to be trained in conventional toolsets like PyTorch and TensorFlow, then converted for execution on the MAX78000 using tools provided by Maxim.
In addition to the memory in the CNN engine, the MAX78000 has large on-chip system memory for the microcontroller core, with 512KB flash and up to 128KB SRAM. Multiple high-speed and low-power communications interfaces are supported, including I2S and a parallel
camera interface (PCIF). The device is available in a 81-pin CTBGA (8mm x 8mm, 0.8mm pitch) package.
Product image
Product parameters
| type | describe |
| category | Integrated Circuit (IC) Embedded Microcontroller |
| manufacturer | Analog Devices Inc./Maxim Integrated |
| pack | tray |
| Core processor | ARM® Cortex®-M4 |
| Kernel specifications | 32-bit dual core |
| speed | 100MHz |
| Connectivity | line,I²C,PCIF,SPI,UART/USART |
| peripheral | DMA,I²S,POR,PWM,WDT |
| I/O count | 52 |
| Program storage capacity | 512KB(512K x 8) |
| Program Memory Type | built-in flash |
| RAM size | 128K x 8 |
| Voltage Supply (Vcc/Vdd) | 1.71V ~ 3.6V |
| converter | A/D 8x10b sigma-delta |
| Oscillator Type | internal |
| operation temperature | -40°C ~ 105°C(TA) |
| Installation type | Surface mount type |
Company Details
Business Type:
Distributor/Wholesaler,Agent,Seller
Year Established:
2018
Total Annual:
2000000-400000000
Employee Number:
80~100
Ecer Certification：
Site Member
Dingcen International (Hong Kong) Co., Ltd. specializes in the IC series products, which are widely used in various fields such as aerospace, communication, Internet of Things, automotive electronics, electronic equipment, instrumentation, security and fire protection, medical equipment, etc. We ... Dingcen International (Hong Kong) Co., Ltd. specializes in the IC series products, which are widely used in various fields such as aerospace, communication, Internet of Things, automotive electronics, electronic equipment, instrumentation, security and fire protection, medical equipment, etc. We ...
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