A Mixed Mode Self-Programming Neural System-on-Chip for Real-Time Applications

نویسندگان

  • Khurram Waheed
  • Fathi M. Salam
چکیده

The paper provides an overview of the development of a self-learning computing chip in the new 0.18 micron copper technology. The chip realizes an architecture that achieves the task of self-learning execution times in the micro to milli seconds. The core consists of basic building blocks of 4quadrant multipliers, trans-conductance amplifiers, and active load resistances, for analog (forward-) network processing and learning modules. Super-imposed on the processing network are digital memory and control modules composed of D-Flip-flops, ADC, Multiplying D/A Converter (MDAC), and comparators for parameter (weight) storage, logical control and analog/digital conversions. The single System-on-Chip design impacts several domains of critical applications that include nano-scale biotechnology, automotive sensing, control and actuation, wireless communications, image feature extraction and pattern matching etc

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تاریخ انتشار 2001