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Aerospace & Defense

 
FRACTALS Logo
FRACTALS is a Program between
GSI Technology and SHREC.

GSI Technology's inaugural projects in the Aerospace and Defense industry are groups of Radiation-Hardened and Radiation-Tolerant synchronous SRAMs:
 

  • A family of SigmaQuad-II+ products: available in 288Mb, 144Mb, and 72Mb densities, x18 and x36 configurations, On-Die Termination (ODT), and up to 350 MHz performance

 

  • A family of SyncBurst & NBT products: available in 144Mb, 72Mb, and 36Mb densities, x18 and x36 configurations, and up to 333 MHz performance


These Rad-Hard SRAMs are expected to serve as a critical element for advanced systems that leverage leading-edge FPGAs, ADCs, and DACs; but until now lacked the high density, high performance, and power efficiency that our outstanding memory products bring. These devices are qualified to Class-Q and Class-V levels to meet the rigorous requirements of aerospace and defense customers. 


For our satellite and defense customers that have been anxiously awaiting an alternative to current Rad-Hard memory solutions, our Rad-Hard SRAMs leverage our proven commercial technology and architecture with radiation-hardening, creating an efficient, high performance, leading-edge memory at the 40nm technology node.

For less robust applications, GSI offers Radiation-Tolerant SRAMs, as well.


For more information regarding this exciting new technology, please contact GSI at aerospace@gsitechnology.com.

Articles--NEW!
Why Space Needs Artificial Intelligence
Radiation Tolerance Meets Commercial Space
Rad-Hard Datasheets
Burst of 2
SigmaQuad-II+
(288Mb/144Mb/72Mb)
Burst of 4
SigmaQuad-II+
(288Mb/144Mb/72Mb)
Synchronous Burst
(144Mb/72Mb/36Mb)
No Bus Turnaround
(144Mb/72Mb/36Mb)
Rad-Tolerant Datasheets
Burst of 2
SigmaQuad-II+
(288Mb/144Mb/72Mb)
Burst of 4
SigmaQuad-II+
(288Mb/144Mb/72Mb)
Synchronous Burst
(144Mb/72Mb/36Mb)
No Bus Turnaround
(144Mb/72Mb/36Mb)
Blogs
Getting Started with Satellite Data Processing
Classification with Similarity Search: Fast and Accurate Radio Wave Classification Radio Wave Classifier In Python: How I Built A ResNet Radio Wave Classifier With Keras Deep Learning For Radio Waves: Using Residual Neural Networks For Signal Classification An Overview Of Signal Classification: From Fourier Transforms To Deep Neural Networks
Brochure
Military Temp Brochure
Whitepapers
Gemini APU:  Enabling High Performance Billion-Scale Similarity Search In-Place Associative Computing:  A New Concept in Processor Design In-Place Associative Computing:  An Introduction to the APU Software In-Place Computing:  Scaling to 1M Similarity Searches per Second
Presentation
The Aerospace Corp Space Parts Working Group Presentation
(April 2017)