Title :
A two-stage charge-based analog/digital neuron circuit with adjustable weights
Author :
Schmid, Alexandre ; Leblebici, Yusuf ; Mlynek, Daniel
Author_Institution :
Integrated Syst. Center, Swiss Fed. Inst. of Technol., Lausanne, Switzerland
Abstract :
A circuit-level neuron architecture based on the principle of analog charge-based computation of neural functions has been developed with the goals of high-speed processing, adjustable weights, and support of perturbation-based learning algorithms. The two-stage architecture which is composed of nonlinear synapses, driving a linear capacitive soma, has been implemented using a conventional double-polysilicon CMOS technology. The feedforward architecture of the proposed neuron model is shown to synthesize a large number of nonlinear mappings of the 2D-1D space
Keywords :
CMOS integrated circuits; feedforward neural nets; learning (artificial intelligence); mixed analogue-digital integrated circuits; neural chips; neural net architecture; adjustable weights; circuit-level neuron architecture; conventional double-polysilicon CMOS technology; feedforward architecture; linear capacitive soma; nonlinear mappings; nonlinear synapses; two-stage charge-based analog/digital neuron circuit; Analog computers; Artificial neural networks; CMOS technology; Capacitance; Computer architecture; Hardware; Integrated circuit technology; Inverters; Neurons; USA Councils;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.833434
