Implementation of Ternary Weights With Resistive RAM Using a Single Sense Operation Per Synapse

The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a significant lead for reducing the energy consumption of artificial intelligence. To achieve maximum energy efficiency in such systems, logic and memory should be...

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Veröffentlicht in:	IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2021-01, Vol.68 (1), p.138-147
Hauptverfasser:	Laborieux, Axel, Bocquet, Marc, Hirtzlin, Tifenn, Klein, Jacques-Olivier, Nowak, Etienne, Vianello, Elisa, Portal, Jean-Michel, Querlioz, Damien
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Biological neural networks Bit error rate CMOS Computer architecture Electric potential Energy consumption Engineering Sciences Error correction Error correction codes Hardware low voltage operation Memory management Micro and nanotechnologies Microelectronics Neural networks Object recognition quantized neural networks Random access memory Resistance resistive memory sense amplifier Sense amplifiers Task analysis Transistors Voltage
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