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1. ADVANTECH #ad
Advantech ADAM-6050-D, 12DI/6DO IoT Modbus/SNMP/MQTT Ethernet Remote I/O #adADVANTECH #ad
- Intelligent control ability by Peer-to-Peer and GCL function. Flexible user-defined Modbus address. Remote monitoring and control with mobile devices. Group configuration capability for multiple module setup. 12-ch di, 6-ch do, ethernet-based smart I/O.
2. Generic #ad
Google Coral Dev Board #adGeneric #ad
- High speed TensorFlow Lite inferencing. Small footprint. Google edge tpu ML Accelerator Coprocessor. Low power. Nxp i. Mx 8m soc quad-core cortex-A53, plus Cortex-M4F.
3. NXP #ad
Microprocessor, 1.15V to 1.3V, i.MX Family i.MX RT Series, MAPBGA-196 Pack of 5 MIMXRT1052CVL5A, 96KB, 500 MHz, 32bit, MIMXRT1052CVL5A #adNXP #ad
- No. Mpu core size : 32bit. Product range : i. Mx family i. Mx rt1050 series Microprocessors. Program memory Size : 96KB. Of pins : 196Pins. Mpu case style : MAPBGA.
4. Google #ad
Coral Dev Board #adGoogle #ad
- Cpu: nxp i. Mx 8m soc quad cortex-A53, cortex-m4f. Gpu: integrated C Lite Graphics. For example, in a power efficient manner. Supports tensorflow lite: no need to build models from the ground up. Tensorflow lite models can be compiled to run on the edge TPE.
Ram: 1 gb lpddr4. Scale from prototype to production with a removable system-on-module som. Scale from prototype to production: considers your manufacturing needs. The som can be removed from the baseboard, ordered in Bulk, and integrated into your hardware.
Provides a complete system: a single-board computer with SoC + ML + wireless connectivity, all on the board running a derivative of Debian Linux We call Mendel, so you can run your favorite Linux tools with this board. Ml accelerator: Google edge TPU Coprocessor.
A development board to quickly prototype on-device ML products. Performs high-speed ml inferencing: the on-board edge tpu coprocessor is capable of performing 4 trillion operations tera-operations per second tops, it can execute state-of-the-art mobile vision models such as mobilenet V2 AT 400 FPS, using 05 watts for each tops 2 tops per watt. Supports automl vision edge: easily build and deploy Fast, high-accuracy custom image Classification models to your device with automl vision edge.