Learning in Memristor Crossbar-Based Spiking Neural Networks Through Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity

Zheng, Nan; Mazumder, Pinaki

doi:10.1109/tnano.2018.2821131

Cited by 51 publications

(29 citation statements)

References 28 publications

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“…How spike trains encode information is among the most important questions in neuroscience [730] . Both, spike timing and spike frequency have been proposed as modes of information transfer in biological brains [856] and both can codes can be investigated in oxide-based synaptors, neuristors and arrays [857] , [858] , [859] , [860] . Owing to its enhanced energy efficiency, spike timing appears to be a preponderant code in biological brains while frequency codes are believed to be exponentially more costly [861] , [862] , [863] .…”

Section: Neural Codesmentioning

confidence: 99%

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Pérez‐Tomás

2019

Adv Materials Inter

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New device concepts and new computing principles are needed to balance our ever-growing appetite for data and information with the realization of the goals of increased energy efficiency, reduction in CO 2 emissions and the circular economy. Neuromorphic or synaptic electronics is an emerging field of research aiming to overcome the current computer's Von-Neumann bottleneck by building artificial neuronal systems to mimic the extremely energy efficient biological synapses. The introduction of photovoltaic and/or photonic aspects into these neuromorphic architectures will produce self-powered adaptive electronics but may also open up new possibilities in artificial neuroscience, artificial neural communications, sensing and machine learning which would enable, in turn, a new era for computational systems owing to the possibility of attaining high bandwidths with much reduced power consumption. This perspective is focused on recent progress in the implementation of functional oxide thinfilms into photovoltaic and neuromorphic applications towards the envisioned goal of selfpowered photovoltaic neuromorphic systems or a solar brain.

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Section: Neural Codesmentioning

confidence: 99%

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Pérez‐Tomás

2019

Adv Materials Inter

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“…Moreover, memristors are easily tunable for plasticity rules which give rise to a compact implementation of spike time dependent plasticity (STDP) and spike rate dependent plasticity (SRDP) (Bill Legenstein, 2014 ). To demonstrate the application of memristors in machine learning, a memristor based crossbar structure is proposed for handwritten digits recognition with a recognition accuracy of 97.10% for MNIST benchmark (Zheng and Mazumder, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

A Scalable FPGA Architecture for Randomly Connected Networks of Hodgkin-Huxley Neurons

et al. 2018

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Human intelligence relies on the vast number of neurons and their interconnections that form a parallel computing engine. If we tend to design a brain-like machine, we will have no choice but to employ many spiking neurons, each one has a large number of synapses. Such a neuronal network is not only compute-intensive but also memory-intensive. The performance and the configurability of the modern FPGAs make them suitable hardware solutions to deal with these challenges. This paper presents a scalable architecture to simulate a randomly connected network of Hodgkin-Huxley neurons. To demonstrate that our architecture eliminates the need to use a high-end device, we employ the XC7A200T, a member of the mid-range Xilinx Artix®-7 family, as our target device. A set of techniques are proposed to reduce the memory usage and computational requirements. Here we introduce a multi-core architecture in which each core can update the states of a group of neurons stored in its corresponding memory bank. The proposed system uses a novel method to generate the connectivity vectors on the fly instead of storing them in a huge memory. This technique is based on a cyclic permutation of a single prestored connectivity vector per core. Moreover, to reduce both the resource usage and the computational latency even more, a novel approximate two-level counter is introduced to count the number of the spikes at the synapse for the sparse network. The first level is a low cost saturated counter implemented on FPGA lookup tables that reduces the number of inputs to the second level exact adder tree. It, therefore, results in much lower hardware cost for the counter circuit. These techniques along with pipelining make it possible to have a high-performance, scalable architecture, which could be configured for either a real-time simulation of up to 5120 neurons or a large-scale simulation of up to 65536 neurons in an appropriate execution time on a cost-optimized FPGA.

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“…The regular structure of neural networks allows them to be mapped to a resistive crossbar architecture and, consequently, profit from its characteristic benefits. Resistive crossbars are widely investigated to hold the synaptic weight of the network [161]- [165]. MTJs are a particularly well-suited resistive memory technology for weight storage, as they offer a high integration density, low access delay, and a low power consumption [166].…”

Section: C O N C L U S I O N a N D P E R S P E C T I V E Smentioning

confidence: 99%

A Survey of Test and Reliability Solutions for Magnetic Random Access Memories

et al. 2021

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| Memories occupy most of the silicon area in nowadays' system-on-chips and contribute to a significant part of system power consumption. Though widely used, nonvolatile Flash memories still suffer from several drawbacks. Magnetic random access memories (MRAMs) have the potential to mitigate most of the Flash shortcomings. Moreover, it is predicted that they could be used for DRAM and SRAM replacement. However, they are prone to manufacturing defects and runtime failures as any other type of memory. This article provides an up-to-date and practical coverage of MRAM test and reliability solutions existing in the literature. After some background on existing MRAM technologies, defectiveness and reliability issues are discussed, as well as functional fault models used for MRAM. This article is dedicated to a summarized description of existing test and reliability improvement methods developed Manuscript

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Learning in Memristor Crossbar-Based Spiking Neural Networks Through Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity

Cited by 51 publications

References 28 publications

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

A Scalable FPGA Architecture for Randomly Connected Networks of Hodgkin-Huxley Neurons

A Survey of Test and Reliability Solutions for Magnetic Random Access Memories

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