Literature DB >> 18244572

Compact low-power calibration mini-DACs for neural arrays with programmable weights.

B Linares-Barranco1, T Serrano-Gotarredona, R Serrano-Gotarredona.   

Abstract

This paper considers the viability of compact low-resolution low-power mini digital-to-analog converters (mini-DACs) for use in large arrays of neural type cells, where programmable weights are required. Transistors are biased in weak inversion in order to yield small currents and low power consumptions, a necessity when building large size arrays. One important drawback of weak inversion operation is poor matching between transistors. The resulting effective precision of a fabricated array of 50 DACs turned out to be 47% (1.1 bits), due to transistor mismatch. However, it is possible to combine them two by two in order to build calibrated DACs, thus compensating for inter-DAC mismatch. It is shown experimentally that the precision can be improved easily by a factor of 10 (4.8% or 4.4 bits), which makes these DACs viable for low-resolution applications such as massive arrays of neural processing circuits. A design methodology is provided, and illustrated through examples, to obtain calibrated mini-DACs of a given target precision. As an example application, we show simulation results of using this technique to calibrate an array of digitally controlled integrate-and-fire neurons.

Year:  2003        PMID: 18244572     DOI: 10.1109/TNN.2003.816370

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  4 in total

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Authors:  Qian Liu; Garibaldi Pineda-García; Evangelos Stromatias; Teresa Serrano-Gotarredona; Steve B Furber
Journal:  Front Neurosci       Date:  2016-11-02       Impact factor: 4.677

2.  Neuromorphic silicon neuron circuits.

Authors:  Giacomo Indiveri; Bernabé Linares-Barranco; Tara Julia Hamilton; André van Schaik; Ralph Etienne-Cummings; Tobi Delbruck; Shih-Chii Liu; Piotr Dudek; Philipp Häfliger; Sylvie Renaud; Johannes Schemmel; Gert Cauwenberghs; John Arthur; Kai Hynna; Fopefolu Folowosele; Sylvain Saighi; Teresa Serrano-Gotarredona; Jayawan Wijekoon; Yingxue Wang; Kwabena Boahen
Journal:  Front Neurosci       Date:  2011-05-31       Impact factor: 4.677

3.  Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

Authors:  Evangelos Stromatias; Daniel Neil; Michael Pfeiffer; Francesco Galluppi; Steve B Furber; Shih-Chii Liu
Journal:  Front Neurosci       Date:  2015-07-09       Impact factor: 4.677

4.  Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

Authors:  Shaista Hussain; Arindam Basu
Journal:  Front Neurosci       Date:  2016-03-31       Impact factor: 4.677

  4 in total

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