7 TOPS/W Cellular Neural Network Processor Core for Intelligent Internet-of-Things

Martin Villemur, Pedro Julian, Tomas Figliolia, Andreas G. Andreou

Research output: Contribution to journalArticlepeer-review


We discuss the architecture, implementation and testing of a simplicial Cellular Neural Network (CNN) vector processor core aimed at vision oriented intelligent Internet-of-Things (IoT) devices. The architecture comprises a linear array of 64 processing elements (PE), each connected to a 4 neighbor clique operating on 8-bit input and state data. A 3-bit simplicial parameter, allows multilevel function approximation and extends the functionality over previously reported chips. Input data vectors are stored in two 64 × 64 × 8 -bit data caches. The chip is synthesized from a custom designed ultra low voltage CMOS library and fabricated in a 55nm CMOS technology. Dynamic voltage/frequency scaling allows operation at power supplies between 0.5 and 1.2 Volts allowing for a tradeoff between speed and power. The fabricated chip achieves an overall performance of 7.05 TOPS/W at 732fps, with a dynamic energy efficiency of 12.2fJ per operation (OP) at 1.2 Volts.
Original languageEnglish
Article number7
Pages (from-to)1324-1328
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number7
Publication statusPublished - 1 Jul 2020
Externally publishedYes


  • Sensors
  • Arrays
  • Cellular neural networks
  • Vector processors
  • Image edge detection


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