The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

DeYong, M.; Findley, Randall; Fields, Chris

doi:10.1109/72.129409

Cited by 31 publications

(7 citation statements)

References 12 publications

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“…Several examples of VLSI WTA networks of spiking neurons can be found in the literature [2,15,24,37,40,51]. In 1992, De Yong et al [24] proposed a VLSI WTA spiking network consisting of four neurons.…”

Section: Soft Winner-take-all Circuitsmentioning

confidence: 99%

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Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

2009

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Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology. Remarkable progress has been made recently, and complex artificial neural sensory-motor systems can be built using this technology. Today, however, NE stands before a large conceptual challenge that must be met before there will be significant progress toward an age of genuinely intelligent neuromorphic machines. The challenge is to bridge the gap from reactive systems to ones that are cognitive in quality. In this paper, we describe recent advancements in NE, and present examples of neuromorphic circuits that can be used as tools to address this challenge. Specifically, we show how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilities.

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Section: Soft Winner-take-all Circuitsmentioning

confidence: 99%

“…In 1992, De Yong et al [24] proposed a VLSI WTA spiking network consisting of four neurons. The authors implemented the WTA mechanism through all-to-all inhibitory connections.…”

Section: Soft Winner-take-all Circuitsmentioning

confidence: 99%

Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

2009

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“…Therefore, the development of highly scalable and low-power electronic synapse that can mimic the synaptic functions of a biological synapse is important for the development of a versatile, large-scale neuromorphic system. Various efforts have been devoted to developing the electronic synapse by exploiting very-large-scale integrated (VLSI) analog complementary metal-oxide-semiconductor (CMOS) circuit, mixed analog/digital circuit, and CMOS-based application specific integrated circuit (ASIC). − These approaches require multiple transistors to realize one electronic synapse, thus resulting in large chip area and high power consumption. Therefore, the attractive features of emerging nanoelectronic devices have started to gain attention for resolving the challenges of CMOS-based approaches.…”

Section: Flexible Pv3d3 Memristor For Electronic Synapsementioning

confidence: 99%

Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System

et al. 2019

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With the advent of artificial intelligence (AI), memristors have received significant interest as a synaptic building block for neuromorphic systems, where each synaptic memristor should operate in an analog fashion, exhibiting multilevel accessible conductance states. Here, we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament. With the quantized conductance states observed in the flexible pV3D3 memristor, analog potentiation and depression characteristics of the memristive synapse are obtained through the growth of atomically thin Cu filament and lateral dissolution of the filament via dominant electric field effect, respectively. The face classification capability of our memristor is evaluated via simulation using an artificial neural network consisting of pV3D3 memristor synapses. These results will encourage the development of soft neuromorphic intelligent systems.

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“…Typically hybrid implementations use analog neurons taking advantage of their smaller size and lower power consumption and use digital memories for permanent weight storage [85,86]. The mixed-signal design of the analog neurons with the digital memories on the same die introduces a lot of noise problems and requires isolation of the sensitive analog parts from the noisy digital parts using techniques such as guard rings.…”

Section: Hybrid Neural Hardware Implementationsmentioning

confidence: 99%

Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments

Merchant

Peterson

2010

VLSI Design

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Dedicated hardware implementations of artificial neural networks promise to provide faster, lower-power operation when compared to software implementations executing on microprocessors, but rarely do these implementations have the flexibility to adapt and train online under dynamic conditions. A typical design process for artificial neural networks involves offline training using software simulations and synthesis and hardware implementation of the obtained network offline. This paper presents a design of block-based neural networks (BbNNs) on FPGAs capable of dynamic adaptation and online training. Specifically the network structure and the internal parameters, the two pieces of the multiparametric evolution of the BbNNs, can be adapted intrinsically, in-field under the control of the training algorithm. This ability enables deployment of the platform in dynamic environments, thereby significantly expanding the range of target applications, deployment lifetimes, and system reliability. The potential and functionality of the platform are demonstrated using several case studies.

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The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

Cited by 31 publications

References 12 publications

Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System

Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments

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