TX93-5210

Ka-Ro electronics
951-TX93-5210
TX93-5210

Mfr.:

Description:
Computer-On-Modules - COM The TX93 is a member of the TX Module family.

Datasheet:
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More Information
Learn more about Ka-Ro electronics TX93-5210
Product available only to OEM/EMS and design business customers. Product is not shipped to consumers in the EU or the UK

In Stock: 9

Stock:
9 Can Dispatch Immediately
Factory Lead Time:
5 Weeks Estimated factory production time for quantities greater than shown.
Minimum: 1   Multiples: 1
Unit Price:
-,-- kr.
Ext. Price:
-,-- kr.
Est. Tariff:
This Product Ships FREE

Pricing (DKK)

Qty. Unit Price
Ext. Price
751,67 kr. 751,67 kr.

Product Attribute Attribute Value Select Attribute
Ka-Ro electronics
Product Category: Computer-On-Modules - COM
Delivery Restrictions:
 Product available only to OEM/EMS and design business customers. Product is not shipped to consumers in the EU or the UK
RoHS:  
NXP
i.MX 93
1.5 GHz
1 GB
1 GB
3.3 V to 5 V
I2C, SPI, UART, USB
- 25 C
+ 85 C
68 mm x 26 mm x 4 mm
Brand: Ka-Ro electronics
Flash: 2 MB
Memory Size: 2 MB
Memory Type: Flash
Product Type: Computer-On-Modules - COM
Series: KaRo Computer-On-Module
Factory Pack Quantity: 1
Subcategory: Computing
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Attributes selected: 0

Datasheet

TARIC:
8471500000
CAHTS:
8471500090
USHTS:
8471500150
JPHTS:
847150000
MXHTS:
8471500100
ECCN:
5A992.C

TX93 Computer-on-Module (COM)

Ka-Ro Electronics TX93 Computer-on-Module (COM) features a 1.5GHz Dual ARM® Cortex®-A55 processor based on the NXP i.MX 93. The i.MX 93 applications processors deliver efficient machine learning (ML) acceleration, energy flex architecture, and state-of-the-art security. Based on Arm’s DynamIQ technology, the integrated, scalable Cortex-A55 core features the latest Armv8-A architecture extensions with dedicated instructions to accelerate machine learning (ML). A dedicated Ethos-U65 neural processing unit (NPU) delivers performance and efficiency with an optimized footprint. The highly scalable design enables developers to create high-performance, cost-effective, and energy-efficient ML applications.

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