Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators

Glukhov, Artem; Milo, Valerio; Baroni, Andrea; Lepri, Nicola; Zambelli, Cristian; Olivo, P.; Pérez, Eduardo; Wenger, Christian; Ielmini, Daniele

doi:10.1109/irps48227.2022.9764497

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Drift is also observed The simulated relative current error of the MVM product as a function of the matrix size. Device parameters were extracted from [79], [166], [184], [185].…”

Section: Memory Nonideality and Metricmentioning

confidence: 99%

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In-Memory Computing for Machine Learning and Deep Learning

Lepri

Glukhov

Cattaneo

et al. 2023

IEEE J. Electron Devices Soc.

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In-memory computing (IMC) aims at executing numerical operations via physical processes, such as current summation and charge collection, thus accelerating common computing tasks including the matrix-vector multiplication. While extremely promising for memory-intensive processing such as machine learning and deep learning, the IMC design and realization must face significant challenges due to device and circuit nonidealities. This work provides an overview of the research trends and options for IMC-based implementations of deep learning accelerators with emerging memory technologies. The device technologies, the computing primitives, and the digital/analog/mixed design approaches are presented. Finally, the major device issues and metrics for IMC are discussed and benchmarked.

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“…Drift is also observed The simulated relative current error of the MVM product as a function of the matrix size. Device parameters were extracted from [79], [166], [184], [185].…”

Section: Memory Nonideality and Metricmentioning

confidence: 99%

“…13a shows a correlation plot between the average conductance value G and the standard deviation σ G . Data were obtained for various NVM devices, including FeFET [79], PCM [185], RRAM [166], and STT-MRAM [184]. The conductance G should be minimized to reduce readout currents, hence energy consumption and IR drop effects.…”

Section: Memory Nonideality and Metricmentioning

confidence: 99%

In-Memory Computing for Machine Learning and Deep Learning

Lepri

Glukhov

Cattaneo

et al. 2023

IEEE J. Electron Devices Soc.

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“…In general, a differential synapse, including two RRAM devices with opposite currents to represent the positive and negative components of the synaptic weight, is recommended to achieve a highly precise zero weight. This is shown in figure 15(a), illustrating a differential synapse where the two opposite currents are obtained by biasing the two RRAM devices with opposite voltage, so that the overall weight is given by W = G + − G − , where G + and G − are the conductance values of the two devices in the differential pair [127]. Given the distribution of programmed conductance in figure 15(b), obtained by the IGVVA10 algorithm, one can properly combine the LRS levels L 1 -L 9 to realize the differential weight distribution in figure 15(c).…”

Section: Challenges Solutions and Perspectivesmentioning

confidence: 99%

HfO₂-based resistive switching memory devices for neuromorphic computing

Brivio

Spiga

Ielmini

2022

Neuromorph. Comput. Eng.

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HfO2-based resistive switching memory (RRAM) combines several outstanding properties, such as high scalability, fast switching speed, low power, compatibility with complementary metal-oxide-semiconductor technology, with possible high-density or three-dimensional integration. Therefore, today, HfO2 RRAMs have attracted a strong interest for applications in neuromorphic engineering, in particular for the development of artificial synapses in neural networks. This review provides an overview of the structure, the properties and the applications of HfO2-based RRAM in neuromorphic computing. Both widely investigated applications of nonvolatile devices and pioneering works about volatile devices are reviewed. The RRAM device is first introduced, describing the switching mechanisms associated to filamentary path of HfO2 defects such as oxygen vacancies. The RRAM programming algorithms are described for high-precision multilevel operation, analog weight update in synaptic applications and for exploiting the resistance dynamics of volatile devices. Finally, the neuromorphic applications are presented, illustrating both artificial neural networks with supervised training and with multilevel, binary or stochastic weights. Spiking neural networks are then presented for applications ranging from unsupervised training to spatio-temporal recognition. From this overview, HfO2-based RRAM appears as a mature technology for a broad range of neuromorphic computing systems.

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“…The technology enabler for IMC architectures has been identified in high density crossbar arrays based on non-volatile memory devices (see Fig. 1a), among which stands out the resistiveswitching non-volatile memory (RRAM) [5]- [7]. Crosspoint arrays of RRAM elements are in fact able to achieve massive parallelism in performing Matrix-Vector-Multiplication (MVM) through the application of the Ohm's and Kirchoff's physical laws in the analog domain [8]- [11].…”

mentioning

confidence: 99%

Low Conductance State Drift Characterization and Mitigation in Resistive Switching Memories (RRAM) for Artificial Neural Networks

Baroni

Glukhov

Pérez

et al. 2022

IEEE Trans. Device Mater. Relib.

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The crossbar structure of Resistive-switching random access memory (RRAM) arrays enabled the In-Memory Computing circuits paradigm, since they imply the native acceleration of a crucial operations in this scenario, namely the Matrix-Vector-Multiplication (MVM). However, RRAM arrays are affected by several issues materializing in conductance variations that might cause severe performance degradation. A critical one is related to the drift of the low conductance states appearing immediately at the end of program and verify algorithms that are mandatory for an accurate multi-level conductance operation.In this work, we analyze the benefits of a new programming algorithm that embodies Set and Reset switching operations to achieve better conductance control and lower variability. Data retention analysis performed with different temperatures for 168 hours evidence its superior performance with respect to standard programming approach. Finally, we explored the benefits of using our methodology at a higher abstraction level, through the simulation of an Artificial Neural Network for image recognition task (MNIST dataset). The accuracy achieved shows higher performance stability over temperature and time.

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Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators

Cited by 7 publications

References 19 publications

In-Memory Computing for Machine Learning and Deep Learning

In-Memory Computing for Machine Learning and Deep Learning

HfO₂-based resistive switching memory devices for neuromorphic computing

Low Conductance State Drift Characterization and Mitigation in Resistive Switching Memories (RRAM) for Artificial Neural Networks

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Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators

Cited by 7 publications

References 19 publications

In-Memory Computing for Machine Learning and Deep Learning

In-Memory Computing for Machine Learning and Deep Learning

HfO2-based resistive switching memory devices for neuromorphic computing

Low Conductance State Drift Characterization and Mitigation in Resistive Switching Memories (RRAM) for Artificial Neural Networks

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HfO₂-based resistive switching memory devices for neuromorphic computing