Evaluation Boards - Embedded - MCU, DSP

Image Part Number Description / PDF Quantity Rfq
102991335

102991335

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SIPEED M1N MODULE AI DEVELOPMENT

15

102991302

102991302

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SIPEED LONGAN NANO - RISC-V GD32

0

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102991279

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ROCK PI 4 MODEL B 2GB

0

102110014

102110014

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ARCH MAX STM32F407 EVAL BRD

0

107990193

107990193

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RESPEAKER USB MIC ARRAY

169

110991189

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SIPEED M1 DOCK SUIT EVAL BRD

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102010248

102010248

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SEEEDUINO CORTEX-M0+

25

102110362

102110362

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BEAGLEBONE AI

57

102991003

102991003

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MICRO:BIT TELEC VERSION EVAL BRD

0

102110267

102110267

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AZURE SPHERE MT3620 MINI DEV BRD

45

102010168

102010168

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SEEEDUINO LOTUS V1.1 ATMEGA328

19

110991191

110991191

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SIPEED MAIX GO SUIT RISCV AI+IOT

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102110483

102110483

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NVIDIA JETSON NANO 2GB DEVELOPER

0

102010048

102010048

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BEAGLEBONE GREEN WIRELESS

1163

107990053

107990053

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RESPEAKER MIC ARRAY V2.0

232

102010388

102010388

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SEEEDUINO XIAO (PRE-SOLDERED)

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110991188

110991188

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SIPEED M1W DOCK SUIT EVAL BRD

51

102010268

102010268

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SEEEDUINO NANO

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102110425

102110425

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SIPEED MAIXCUBE ALL-IN-ONE AI DE

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114992604

114992604

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SIPEED MAIX-II DOCK - DEEP LEARN

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Evaluation Boards - Embedded - MCU, DSP

1. Overview

Evaluation Boards (Dev Boards) for Embedded MCUs (Microcontroller Units) and DSPs (Digital Signal Processors) are specialized hardware platforms designed to facilitate the development, testing, and prototyping of embedded systems. These boards provide a physical environment to validate processor capabilities, peripheral integration, and software algorithms before final product deployment. They play a critical role in accelerating development cycles for applications ranging from IoT devices to industrial automation systems.

2. Major Types and Functional Classification

TypeFunctional FeaturesApplication Examples
MCU Evaluation BoardsARM Cortex-M series, integrated peripherals (UART, SPI, I2C), low-power modesSmart sensors, wearables, home automation
DSP Development KitsHigh-speed floating-point processing, SIMD instructions, real-time signal analysisAudio processing, radar systems, motor control
SoC Embedded BoardsIntegrated CPU+GPU+FPGA, multimedia acceleration, OS supportEdge computing, robotics, automotive infotainment
FPGA-based Prototyping BoardsReconfigurable logic, hardware-software co-design, high-speed I/O5G communication, AI inference accelerators

3. Structure and Components

Typical evaluation boards consist of:

  • PCB base with processor/microcontroller soldered onboard
  • Memory modules (SRAM, Flash, DDR)
  • Debugging interfaces (JTAG, SWD, UART)
  • Power management unit (voltage regulators, PMICs)
  • Peripheral connectors (GPIO, ADC/DAC, Ethernet)
  • Expansion headers for add-on modules (shields, PMODs)
  • Onboard sensors/actuators (depending on application focus)

4. Key Technical Specifications

ParameterImportance
Processor ArchitectureDetermines computational capabilities and software ecosystem compatibility
Maximum Clock FrequencyImpacts processing speed and real-time performance
Memory BandwidthAffects data throughput for signal processing applications
Peripheral IntegrationReduces external component requirements and system complexity
Power ConsumptionCritical for battery-powered and thermal-constrained applications
Debugging CapabilitiesEnables efficient firmware development and hardware verification

5. Application Fields

Key industries utilizing evaluation boards:

  • Industrial Automation: PLCs, motor drives, predictive maintenance systems
  • Consumer Electronics: Smart home devices, AR/VR headsets
  • Automotive: ADAS prototyping, ECU development
  • Medical: Portable diagnostic equipment, wearable health monitors
  • Communications: 5G baseband processing, software-defined radios
  • Energy: Smart grid controllers, solar inverters

6. Leading Manufacturers and Representative Products

ManufacturerProduct SeriesKey Features
STMicroelectronicsSTM32 Nucleo SeriesARM Cortex-M cores, Arduino compatibility, mbed OS support
Texas InstrumentsTMDX SeriesC2000 DSPs for power electronics, Code Composer Studio integration
NXP Semiconductorsi.MX RT SeriesARM Cortex-M7 based crossover processors, LCD interface support
XilinxZynq UltraScale+ MPSoCARM Cortex-A53 + FPGA fabric, AI acceleration with DPU

7. Selection Guidelines

Key considerations when choosing evaluation boards:

  • Match processor architecture to target application requirements (e.g., ARM for general-purpose, DSP for signal processing)
  • Verify peripheral compatibility with system design (number of timers, communication interfaces)
  • Assess expansion capabilities for future upgrades
  • Evaluate software toolchain maturity (IDE, compilers, RTOS support)
  • Consider power consumption specifications for end-application scenarios
  • Check available community resources and technical documentation

8. Industry Trend Analysis

Emerging trends shaping evaluation board development:

  • Increased integration of AI acceleration cores (e.g., Google Edge TPU integration)
  • Rise of RISC-V based evaluation platforms for customizable computing
  • Enhanced security features (trusted execution environments, hardware encryption)
  • Development of low-power wide-area network (LPWAN) enabled boards for IoT
  • Adoption of heterogeneous computing architectures (CPU+GPU+DSP+FPGA)
  • Cloud-connected evaluation platforms for remote testing and collaboration
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