SparkFun RedBoard Edge
Texas Instruments TM4C1294XL
TinyLily Mini
TinyPICO
Asus Tinker Edge R
Asus Tinker Edge T
Banana Pi M2 Berry
BeagleBone Black
BeagleBone Blue
F-Secure USB Armory Mk II
Google Coral Dev Board Mini
$22
$21
$10
$20
$250
$160
$36
$55
$82
$140
$99
4"×1.5"
4.9"×2.2"
0.55" dia.
0.71"×1.26"
3.9"×2.8"
3.37"×2.13"
3.6"×2.4"
3.4"×2.1"
3.4"×2.1"
2.5"×0.75"×0.25"
1.88"×2.51"
Arduino IDE
Energia, Code Composer, others
Arduino IDE
Arduino IDE, MicroPython,
Espressif
Debian 9, Android 9
Mendel Linux
Linux
Linux
Debian Linux with Cloud9 IDE
and libroboticscape
Linux
Mendel Linux (Debian based)
16MHz
100MHz
8MHz
240MHz
1.8GHz (Dual
Core) + 1.4GHz
(Quad Core)
1.5GHz
1GHz
1GHz
1GHz
900MHz
1.3GHz
8-bit Atmega328
120MHz 32-bit Cortex-M4
8-bit Atmega328P
32-bit ESP32
64-bit Rockchip RK3399Pro
(dual-core Cortex-A72 @ 1.8GHz,
quad-core Cortex-A53 @ 1.4GHz)
64-bit NXP i.MX 8M
32-bit quad-core
Cortex-A7 V40
32-bit AM335X
Cortex-A8
32-bit Cortex-A8, TI Programmable
Real-time Units
NXP i.MX6ULZ
64-bit MediaTek i300
32KB flash, 2KB DRAM
1MB flash, 256KB RAM,
6KB EEPROM
32KB flash
4MB PSRAM
Dual-CH LPDDR4 4GB (System) +
LPDDR3 2GB (NPU)
LPDDR4 1GB
1GB DDR3 SDRAM
4GB eMMC flash
512MB RAM,
4GB eMMC flash
512MB RAM
2GB
BeagleBone AI
$99 3.4"×2.1" Debian GNU/Linux 1.5GHz
32-bit Sitara AM5729 Cortex-A15 with
dual C66x DSP, quad 32-bit Cortex-M4
coprocessors, dual-SGX544 GPU
1GB RAM, 16GB flash
BeagleBoard PocketBeagle
BeagleBoard-X15
$25
$270
2.2"×1.4"
4"×4.2"
Linux
Linux
1GHz
1GHz
32-bit Cortex-A8
32-bit AM5728
Cortex-A15
512MB DDR3
4GB 8-bit eMMC flash
Board Name
Price Dimensions Software Clock Speed Processor
Memory
MICROCONTROLLERS (MCU)
SINGLE-BOARD COMPUTERS (SBC)
Asus Tinker Board S
$85 3.37"×2.125"
Debian Linux (Linaro),
Android 6 & 7
1.8GHz
32-bit Rockchip RK3288-
bit ATSAMD21
2GB Dual Channel DDR3
SparkFun RedBoard Artemis
$20 2.7"×2.1" Arduino IDE, Ambiq Apollo SDK 96MHz 32-bit Ambiq Apollo3 Cortex-M4F 384KB RAM, 1MB flash
DFRobot LattePandaV1
$89 2.75"×3.42" Windows 10 1.92GHz 64-bit quad-core Intel Z8350 2GB RAM, 32GB flash
Nvidia Jetson Nano Developer Kit
Jetson Nano is built for flexibility. It runs all the popular
machine learning frameworks. If drawing 10W of power
is too much for your creation, it can down-power half its
cores to run at 5W. And if you discover that you just can’t
do without faster performance after making version 1 of
your creation on Nano, the same deep learning models
you built on Nano will run on the higher-end Jetsons, no
changes needed. If you’ve been interested in trying out
Deep Learning to make a self-driven robot or other trained-
rather-than-programmed creation, this kit occupies a
sweet spot of power and price.
Reviews by Mel Ho, Sam Brown, Caleb Kraft, and Mike Senese
ONES TO WATCH
TREND SPOTTING
8
GUIDE TO BOARDS 2020
Grab your boards at digikey.com/boards
M74_Outsert01-12_BoardGuide2020_F1.indd 8M74_Outsert01-12_BoardGuide2020_F1.indd 8 7/21/20 1:15 PM7/21/20 1:15 PM
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