Evaluation and Demonstration Boards and Kits

Image Part Number Description / PDF Quantity Rfq
ADNK-6530

ADNK-6530

Broadcom

KIT SAMPLE OPT MOUSE ADNB-6532

0

PEX8618BA-BB8U1D RDK

PEX8618BA-BB8U1D RDK

Broadcom

BSBRD+CM107+CM108+CM109+CABLE AD

0

HFBR-0400

HFBR-0400

Broadcom

KIT EVAL FIBER OPTIC 5MBD

0

ADNK-6013-SP01

ADNK-6013-SP01

Broadcom

KIT REF DES OPT MOUSE ADNS-6010

0

BCM985100

BCM985100

Broadcom

ASSY TOP BCM985100

0

PEX8616-BB RDK

PEX8616-BB RDK

Broadcom

KIT - BSBRD+CONFIG MOD 0096+BRKO

0

PEX8614BA-BB4U1D RDK

PEX8614BA-BB4U1D RDK

Broadcom

BSBRD+CM107+2 CM108+CABLE ADPTR

0

BCM9HMCSPACER

BCM9HMCSPACER

Broadcom

ASSY TOP BCM9HMCSPACER

0

ADNK-5023-SP02

ADNK-5023-SP02

Broadcom

KIT REF DES OPTICAL MOUSE A5023

0

ADNK-7053-ND24

ADNK-7053-ND24

Broadcom

KIT REFERENCE DESIGN ADNS-7050

0

OXUF943SE-LQCG

OXUF943SE-LQCG

Broadcom

OXUF943SE-LQCG

0

HFBR-0410

HFBR-0410

Broadcom

KIT EVAL FIBER OPTIC 5MBD

0

BCM9PV710

BCM9PV710

Broadcom

DEVELOPMENT KIT FOR PVG710

0

BCM94718NR

BCM94718NR

Broadcom

ASSY TOP BCM94718NR

0

BCM985650TIQRIQ

BCM985650TIQRIQ

Broadcom

ASSY TOP BCM985650TIQRIQ

0

BCM985810HBSI

BCM985810HBSI

Broadcom

DEVELOPMENT KIT FOR BCM85810 HIG

0

L5-25413-15

L5-25413-15

Broadcom

MR SAS 9266-4I SINGLE KIT D1

0

PEX8625-1U1D BB RDK

PEX8625-1U1D BB RDK

Broadcom

8696 BSBRD+CM091+2 CM093+CM107+2

0

PEX8780-AB RDK

PEX8780-AB RDK

Broadcom

8780 BSBRD + 1 ADPTR CRD 8732 +

0

PEX8114-BD RDK-F

PEX8114-BD RDK-F

Broadcom

PEX8114-BD RDK-FORWARD

0

Evaluation and Demonstration Boards and Kits

Evaluation and Demonstration Boards and Kits are hardware platforms designed to facilitate the development, testing, and demonstration of electronic systems. They serve as critical tools for engineers and developers to prototype applications, validate designs, and accelerate time-to-market. These boards integrate processors, sensors, communication interfaces, and software ecosystems, enabling rapid experimentation across diverse industries such as IoT, automotive, and industrial automation.

TypeFunctional FeaturesApplication Examples
Microcontroller Development BoardsEmbedded CPUs, GPIOs, integrated peripheralsIoT devices, robotics
FPGA Evaluation BoardsReconfigurable logic, high-speed interfacesCommunication systems, AI accelerators
Sensor Expansion KitsMulti-sensor integration (temperature, motion, etc.)Smart agriculture, environmental monitoring
Wireless Communication ModulesBluetooth/Wi-Fi/LoRa protocols, antenna interfacesConnected healthcare, smart cities

Typical architecture includes: - Processing Units: Microcontrollers, FPGAs, or SoCs - Memory: RAM, Flash, EEPROM - Interfaces: USB, UART, SPI, I2C, Ethernet - Power Management: Regulators, battery connectors - Software Stack: SDKs, device drivers, IDEs Physical designs often feature standardized form factors (e.g., Arduino Uno, Raspberry Pi HATs) for modular expansion.

ParameterDescription
Processor Performance (MHz/GHz)Determines computational capability
Memory Capacity (RAM/Flash)Affects program complexity and data storage
Interface TypesDictates peripheral compatibility
Power Consumption (mW/MHz)Critical for battery-operated devices
Operating Temperature (-40 C to +85 C)Defines environmental durability

- Internet of Things (IoT): Smart home controllers, edge AI nodes - Automotive: ADAS sensor fusion platforms - Industrial Automation: PLC controllers, predictive maintenance systems - Consumer Electronics: Wearables, AR/VR prototypes

ManufacturerRepresentative Products
STMicroelectronicsSTM32 Nucleo Series, SensorTile Kit
IntelIntel Edison, Movidius Neural Compute Stick
XilinxZynq UltraScale+ MPSoC Evaluation Kit
ArduinoArduino MKR Series, Nano 33 IoT

Key considerations: 1. Match processor capabilities to application complexity 2. Verify interface compatibility with target peripherals 3. Assess software ecosystem maturity (e.g., ROS support) 4. Evaluate power budget requirements 5. Consider long-term availability and community support

- Growing adoption of RISC-V-based evaluation platforms - Integration of AI/ML accelerators in edge computing boards - Expansion of open-source hardware ecosystems - Increased focus on energy-efficient architectures for IoT - Standardization of form factors (e.g., SparkFun's Qwiic system)

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