EXSHINE Part Number: | EX-901-1001 |
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Manufacturer Part Number: | 901-1001 |
Manufacturer / Brand: | Cognimem |
Brief Description: | COGNIBLOX 4K NEURONS W/EXP SDK |
Lead Free Status / RoHS Status: | Lead free / RoHS Compliant |
Condition: | New and unused, Original |
Datasheet Download: | CogniBlox-4K DataSheetCogniBlox Hardware User's Manual |
Application: | - |
Weight: | - |
Alternative Replacement: | - |
Speed | - |
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Size / Dimension | - |
Series | - |
RAM Size | 4MB |
Other Names | 9011001 |
Operating Temperature | - |
Moisture Sensitivity Level (MSL) | 1 (Unlimited) |
Module/Board Type | FPGA, USB Core |
Manufacturer Part Number | 901-1001 |
Flash Size | - |
Expanded Description | - Embedded Module XP2 FT232H 4MB |
Description | COGNIBLOX 4K NEURONS W/EXP SDK |
Core Processor | XP2 |
Connector Type | Spinal, Cardinal |
Co-Processor | FT232H |
Other Names | 9011001 |
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Standard Package | 1 |
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T/T (Bank Transfer) Receiving: 1-4 days. |
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Paypal Receiving: immediately. |
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Western Union Receiving: 1-2 hours. |
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MoneyGram Receiving: 1-2 hours. |
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Alipay Receiving: immediately. |
DHL EXPRESS Delivery time: 1-3 days. |
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FEDEX EXPRESS Delivery time: 1-3 days. |
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UPS EXPRESS Delivery time: 2-4 days. |
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TNT EXPRESS Delivery time: 3-6 days. |
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EMS EXPRESS Delivery time: 7-10 days. |
- CogniMem Technologies, Inc. is an innovative semiconductor company specializing in pattern recognition with general purpose hardware based neural networks. Our solutions are tailored to overcome challenges in machine learning, machine vision, robotics, inspection, industrial controls, intelligent security cameras, medical and automotive sensing/ sensor fusion to data mining, video analytics, data searching and more. CogniMem – “Cognitive Memory” – is the world leader in providing practical cognitive computing for intelligent sensing to data mining applications, offering a significant performance/ watt advantage over traditional approaches.
CogniMem™ Technologies is a fabless semi-conductor company designing components for high speed and parallel pattern recognition. Its research and design efforts target two extreme usage models: