Literature DB >> 23999447

A scalable neural chip with synaptic electronics using CMOS integrated memristors.

Jose M Cruz-Albrecht1, Timothy Derosier, Narayan Srinivasa.   

Abstract

The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal-oxide-semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73 728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior.

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Year:  2013        PMID: 23999447     DOI: 10.1088/0957-4484/24/38/384011

Source DB:  PubMed          Journal:  Nanotechnology        ISSN: 0957-4484            Impact factor:   3.874


  10 in total

1.  Comparative Analysis of Reconfigurable Platforms for Memristor Emulation.

Authors:  Margarita Mayacela; Leonardo Rentería; Luis Contreras; Santiago Medina
Journal:  Materials (Basel)       Date:  2022-06-25       Impact factor: 3.748

2.  A role for neuromorphic processors in therapeutic nervous system stimulation.

Authors:  Corey M Thibeault
Journal:  Front Syst Neurosci       Date:  2014-10-07

3.  PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems.

Authors:  Fabio Stefanini; Emre O Neftci; Sadique Sheik; Giacomo Indiveri
Journal:  Front Neuroinform       Date:  2014-08-29       Impact factor: 4.081

4.  Synaptic plasticity enables adaptive self-tuning critical networks.

Authors:  Nigel Stepp; Dietmar Plenz; Narayan Srinivasa
Journal:  PLoS Comput Biol       Date:  2015-01-15       Impact factor: 4.475

Review 5.  Stochastic Resonance in Organic Electronic Devices.

Authors:  Yoshiharu Suzuki; Naoki Asakawa
Journal:  Polymers (Basel)       Date:  2022-02-15       Impact factor: 4.329

6.  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

7.  Event-driven contrastive divergence for spiking neuromorphic systems.

Authors:  Emre Neftci; Srinjoy Das; Bruno Pedroni; Kenneth Kreutz-Delgado; Gert Cauwenberghs
Journal:  Front Neurosci       Date:  2014-01-30       Impact factor: 4.677

8.  An efficient automated parameter tuning framework for spiking neural networks.

Authors:  Kristofor D Carlson; Jayram Moorkanikara Nageswaran; Nikil Dutt; Jeffrey L Krichmar
Journal:  Front Neurosci       Date:  2014-02-04       Impact factor: 4.677

9.  Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding.

Authors:  Sapan Agarwal; Tu-Thach Quach; Ojas Parekh; Alexander H Hsia; Erik P DeBenedictis; Conrad D James; Matthew J Marinella; James B Aimone
Journal:  Front Neurosci       Date:  2016-01-06       Impact factor: 4.677

10.  Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware.

Authors:  Narayan Srinivasa; Nigel D Stepp; Jose Cruz-Albrecht
Journal:  Front Neurosci       Date:  2015-12-01       Impact factor: 4.677

  10 in total

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