Title :
A reconfigurable low-voltage low-power building block for artificial neural networks
Author :
Lee, S.T. ; Lau, K.T.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
Abstract :
A reconfigurable low-voltage low-power building block for artificial neural networks (ANNs) that can either function as a synapse or a neuron is proposed and analyzed in this paper. The design is based on the current-mode approach and uses the square-law characteristics of a MOS transistor working in saturation. The new building block utilizes I-V converters, current-mirror, and a ±1 V power supply to achieve superior performance. Modularity, ease of interconnectivity, expandability and reconfigurability are the advantages of this building block
Keywords :
CMOS analogue integrated circuits; analogue processing circuits; neural chips; 1 V; I-V converters; MOS transistor; artificial neural networks; current-mirror; current-mode approach; expandability; interconnectivity; modularity; power supply; reconfigurable low-voltage low-power building block; saturation; square-law characteristics; synapse; Analog circuits; Analog computers; Artificial neural networks; Integrated circuit interconnections; LAN interconnection; MOSFETs; Neural networks; Neurons; Power supplies; Threshold voltage;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487379
