Physical aspects of low power synapses based on phase change memory devices

Suri, Manan; Bichler, Olivier; Querlioz, Damien; Traoré, Boubacar; Cueto, O.; Perniola, L.; Sousa, V.; Vuillaume, D.; Gamrat, Christian; DeSalvo, B.

doi:10.1063/1.4749411

Cited by 127 publications

(119 citation statements)

References 48 publications

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“…The black curve shows one representative cycle of the total ~2300 cycles that were applied (grey curves). These measurements show the possibility of reaching many intermediate levels with short pulses during the SET process (between 3 and 5 V), while a stable state is more difficult to obtain during RESET, which dramatically decreases conductivity above 5 V. Asymmetry between SET and RESET modes is typical of dynamic filamentary behavior, and constitutes a general limitation learning schemes must address38.…”

Section: Resultsmentioning

confidence: 96%

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

Lin

Bennett

Cabaret

et al. 2016

Sci Rep

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Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

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Section: Resultsmentioning

confidence: 96%

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

Lin

Bennett

Cabaret

et al. 2016

Sci Rep

Self Cite

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“…In all the following simulations, we present less peripheral circuit details but pay more attention to the influence of dynamic range and precision of memristor device, which are the two key points narrowing the gap between memristive system and HCSM model. Based on the real memristor data in Figure S1, as well as some existing physical models and behavioral models of the memristor (Strukov et al, 2008; Yang et al, 2008; Guan et al, 2012a; Suri et al, 2012; Deng et al, 2015), we build an iron oxide memristor model whose synaptic behavior shows excellent agreement with the real device experiments (Figure S1D). Furthermore, we use SPICE (a standard circuit simulator) to verify the proposed network model of HCSM shown in Figure 3 and Figure S4.…”

Section: Methodsmentioning

confidence: 88%

Hierarchical Chunking of Sequential Memory on Neuromorphic Architecture with Reduced Synaptic Plasticity

Deng

Wang

et al. 2016

Front. Comput. Neurosci.

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Chunking refers to a phenomenon whereby individuals group items together when performing a memory task to improve the performance of sequential memory. In this work, we build a bio-plausible hierarchical chunking of sequential memory (HCSM) model to explain why such improvement happens. We address this issue by linking hierarchical chunking with synaptic plasticity and neuromorphic engineering. We uncover that a chunking mechanism reduces the requirements of synaptic plasticity since it allows applying synapses with narrow dynamic range and low precision to perform a memory task. We validate a hardware version of the model through simulation, based on measured memristor behavior with narrow dynamic range in neuromorphic circuits, which reveals how chunking works and what role it plays in encoding sequential memory. Our work deepens the understanding of sequential memory and enables incorporating it for the investigation of the brain-inspired computing on neuromorphic architecture.

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“…Without an effectively theoretical model, researchers may have to spend tremendous computational and experimental resources to establish the viable technological path towards the success of building PCM-based neurons and synapses. To achieve this goal, Suri et al first proposed a behaviour model to simulate the LTP and LTD behaviours of PCMs using the Ge 2 Sb 2 Te 5 (GST) and GeTe medium [90–92]. In Suri’s model, the conductances of GST and GeTe medium are considered as the synapse weight which can be therefore modified through either ‘SET’ process that corresponds to LTP or ‘RESET’ process indicating LTD.…”

Section: Reviewmentioning

confidence: 99%

Recent Advances on Neuromorphic Systems Using Phase-Change Materials

Wang

Wen

2017

Nanoscale Res Lett

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Realization of brain-like computer has always been human’s ultimate dream. Today, the possibility of having this dream come true has been significantly boosted due to the advent of several emerging non-volatile memory devices. Within these innovative technologies, phase-change memory device has been commonly regarded as the most promising candidate to imitate the biological brain, owing to its excellent scalability, fast switching speed, and low energy consumption. In this context, a detailed review concerning the physical principles of the neuromorphic circuit using phase-change materials as well as a comprehensive introduction of the currently available phase-change neuromorphic prototypes becomes imperative for scientists to continuously progress the technology of artificial neural networks. In this paper, we first present the biological mechanism of human brain, followed by a brief discussion about physical properties of phase-change materials that recently receive a widespread application on non-volatile memory field. We then survey recent research on different types of neuromorphic circuits using phase-change materials in terms of their respective geometrical architecture and physical schemes to reproduce the biological events of human brain, in particular for spike-time-dependent plasticity. The relevant virtues and limitations of these devices are also evaluated. Finally, the future prospect of the neuromorphic circuit based on phase-change technologies is envisioned.

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Physical aspects of low power synapses based on phase change memory devices

Cited by 127 publications

References 48 publications

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

Hierarchical Chunking of Sequential Memory on Neuromorphic Architecture with Reduced Synaptic Plasticity

Recent Advances on Neuromorphic Systems Using Phase-Change Materials

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