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Neural Network PCI Card

 

NNPC-12 Neural Network Card

Modulus is proud to bring you the new NNPC-12 neural network PCI card for financial applications. NNPC-12 provides true MIMD processing for the fastest possible network training, evaluation, and implementation.

The heart of the NNP family is the MIMD Neural Network Processor, which provides the basic unit of processing. Multiple NNPs can operate in parallel through a unique bus and connector design.

PCI, ISA, and VME busses are supported with interface boards that have up to two built in NNPs. Additional NNPs can be stacked on the baseboard. The VME board includes two TMS320C40 DSP Chips.

The boards are supported on a variety of PC and SGI platforms.

Features

  • True Multiple Instruction-Multiple Data (MIMD) architecture
  • Stack to Max of 10 Processors
  • 8,192 Neurons per system
  • 32,768 Connections per processor
  • 32 million multiply-accumulate cycles per second per processor
  • Unique Neural Network Processor Language; neural network tools and compiler provided.
  • Program and run standard paradigms or your own unique paradigm
  • Powerful architecture concept that avoids memory bottlenecks.

System Requirements

The following hardware platforms are supported:

  • PC Platforms running Windows:
    • PC with ISA Bus
    • PC with PCI Bus
  • Silicon Graphics Platforms running IRIX 5.2 and above:
    • Indy using Bit3 Corporation GIO32 to VME bus adaptor
    • Indigo using Bit3 Corporation GIO32 to VME bus adaptor
    • Indigo 2 using internal EISA bus
    • Onyx using internal VME bus
    • Power Series with single IO3 board using internal VME bus
  • VME
    • VME 6U dual TMS320C40 DSP card with the ability to support one to four Neural Network Processors

Software

Available programming tools include an assembler, linker, debugger, and diagnostics program that allow users to quickly design and use networks.

The programming tools  allows rapid prototyping of neural network solutions and integration in real-world applications. The programming tools supports a variety of paradigms trained with supervised and/or unsupervised training algorithms.


 
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System Requirements
CPU Intel Pentium IV 1.5GHz or equivalent
Memory 8 GB DDR2 RAM or higher
Storage None

 

 

 

 
 
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