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Author

Haitong Li

Other affiliations: Peking University
Bio: Haitong Li is an academic researcher from Stanford University. The author has contributed to research in topics: Resistive random-access memory & Neuromorphic engineering. The author has an hindex of 19, co-authored 53 publications receiving 1678 citations. Previous affiliations of Haitong Li include Peking University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained.
Abstract: Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for the fabrication of electronic devices, with electronic nonvolatile memories being those that have received the most attention. The presence and quality of the RS phenomenon in a materials system can be studied using different prototype cells, performing different experiments, displaying different figures of merit, and developing different computational analyses. Therefore, the real usefulness and impact of the findings presented in each study for the RS technology will be also different. This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained. The idea is to help the scientific community to evaluate the real usefulness and impact of an RS study for the development of RS technology. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

441 citations

Journal ArticleDOI
01 Aug 2018
TL;DR: It is shown that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity.
Abstract: Neuromorphic computing systems, which use electronic synapses and neurons, could overcome the energy and throughput limitations of today’s computing architectures. However, electronic devices that can accurately emulate the short- and long-term plasticity learning rules of biological synapses remain limited. Here, we show that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses. The devices can operate in a volatile or non-volatile regime, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity. The behaviour results from a resistive switching mechanism in the h-BN stack, based on the generation of boron vacancies that can be filled by metallic ions from the adjacent electrodes. The power consumption in standby and per transition can reach as low as 0.1 fW and 600 pW, respectively, and with switching times reaching less than 10 ns, demonstrating their potential for use in energy-efficient brain-like computing. Vertically structured electronic synapses, which exhibit both short- and long-term plasticity, can be created using layered two-dimensional hexagonal boron nitride.

420 citations

Journal ArticleDOI
TL;DR: In this article, a physics-based compact model of metal-oxide-based resistive-switching random access memory (RRAM) cell under dc and ac operation modes is presented.
Abstract: A physics-based compact model of metal-oxide-based resistive-switching random access memory (RRAM) cell under dc and ac operation modes is presented. In this model, the conductive filament evolution corresponding to the resistive switching process is modeled by considering the transport behaviors of oxygen vacancies and oxygen ions together with the temperature effect. Both the metallic-like and electron hopping conduction transports are considered to model the conduction of RRAM. The model can reproduce both the typical I-V characteristics of RRAM in high-/low-resistance state (LRS) and the nonlinear characteristics in LRS. Moreover, to accurately model ac operation mode, the effects of parasitic capacitance and resistance are included in our model. The developed compact model is verified and calibrated by measured data in different HfOx-based RRAM devices under dc and ac operation modes. The excellent agreement between the model predictions and experimental results shows a promising prospect of the future implementation of this compact model in large-scale circuit simulation to optimize the design of RRAM.

161 citations

Proceedings ArticleDOI
01 Feb 2018
TL;DR: An end-to-end brain-inspired hyperdimensional (HD) computing nanosystem, effective for cognitive tasks such as language recognition, using heterogeneous integration of multiple emerging nanotechnologies using monolithic 3D integration of carbon nanotube field-effect transistors.
Abstract: We demonstrate an end-to-end brain-inspired hyperdimensional (HD) computing nanosystem, effective for cognitive tasks such as language recognition, using heterogeneous integration of multiple emerging nanotechnologies. It uses monolithic 3D integration of carbon nanotube field-effect transistors (CNFETs, an emerging logic technology with significant energy-delay product (EDP) benefit vs. silicon CMOS [1]) and Resistive RAM (RRAM, an emerging memory that promises dense non-volatile and analog storage [2]). Due to their low fabrication temperature ( 20,000 sentences (6.4 million characters) per language pair. 2. One-shot learning (i.e., learning from few examples) using one text sample (∼100,000 characters) per language. 3. Resilient operation (98% accuracy) despite 78% hardware errors (circuit outputs stuck at 0 or 1). Our HD nanosystem consists of 1,952 CNFETs integrated with 224 RRAM cells.

118 citations

Journal ArticleDOI
Haitong Li1, Peng Huang1, Bin Gao1, Bing Chen1, Xiaoyan Liu1, Jinfeng Kang1 
TL;DR: In this article, a SPICE model of oxide-based resistive random access memory (RRAM) for dc and transient behaviors is developed based on the conductive filament evolution model and implemented in large-scale array simulation.
Abstract: A SPICE model of oxide-based resistive random access memory (RRAM) for dc and transient behaviors is developed based on the conductive filament evolution model and is implemented in large-scale array simulation. The simulations of one transistor-one resistor RRAM array up to 16 kb with wire resistance (Rwire) and capacitance (Cwire) indicate that: 1) resistance-capacitance delay during RESET and leakage current during SET have significant impact on write operations; 2) with array size enlarging, the power dissipation increases during RESET but decreases during SET; and 3) the increased Rwire and Cwire lead to the degradation of high resistance state and the fluctuation of low resistance state, respectively.

115 citations


Cited by
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Journal ArticleDOI
01 Jan 2018
TL;DR: The state of the art in memristor-based electronics is evaluated and the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing is explored.
Abstract: A memristor is a resistive device with an inherent memory. The theoretical concept of a memristor was connected to physically measured devices in 2008 and since then there has been rapid progress in the development of such devices, leading to a series of recent demonstrations of memristor-based neuromorphic hardware systems. Here, we evaluate the state of the art in memristor-based electronics and explore where the future of the field lies. We highlight three areas of potential technological impact: on-chip memory and storage, biologically inspired computing and general-purpose in-memory computing. We analyse the challenges, and possible solutions, associated with scaling the systems up for practical applications, and consider the benefits of scaling the devices down in terms of geometry and also in terms of obtaining fundamental control of the atomic-level dynamics. Finally, we discuss the ways we believe biology will continue to provide guiding principles for device innovation and system optimization in the field. This Perspective evaluates the state of the art in memristor-based electronics and explores the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing.

1,231 citations

Journal ArticleDOI
18 Jun 2016
TL;DR: This work proposes a novel PIM architecture, called PRIME, to accelerate NN applications in ReRAM based main memory, and distinguishes itself from prior work on NN acceleration, with significant performance improvement and energy saving.
Abstract: Processing-in-memory (PIM) is a promising solution to address the "memory wall" challenges for future computer systems. Prior proposed PIM architectures put additional computation logic in or near memory. The emerging metal-oxide resistive random access memory (ReRAM) has showed its potential to be used for main memory. Moreover, with its crossbar array structure, ReRAM can perform matrix-vector multiplication efficiently, and has been widely studied to accelerate neural network (NN) applications. In this work, we propose a novel PIM architecture, called PRIME, to accelerate NN applications in ReRAM based main memory. In PRIME, a portion of ReRAM crossbar arrays can be configured as accelerators for NN applications or as normal memory for a larger memory space. We provide microarchitecture and circuit designs to enable the morphable functions with an insignificant area overhead. We also design a software/hardware interface for software developers to implement various NNs on PRIME. Benefiting from both the PIM architecture and the efficiency of using ReRAM for NN computation, PRIME distinguishes itself from prior work on NN acceleration, with significant performance improvement and energy saving. Our experimental results show that, compared with a state-of-the-art neural processing unit design, PRIME improves the performance by ~2360× and the energy consumption by ~895×, across the evaluated machine learning benchmarks.

1,197 citations

Journal ArticleDOI
01 Jun 2018
TL;DR: This Review Article examines the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, theirresistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation.
Abstract: Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing. This Review Article examines the development of in-memory computing using resistive switching devices.

1,193 citations

Journal ArticleDOI
TL;DR: This Review provides an overview of memory devices and the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.
Abstract: Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing. This Review provides an overview of memory devices and the key computational primitives for in-memory computing, and examines the possibilities of applying this computing approach to a wide range of applications.

841 citations

Proceedings ArticleDOI
01 Feb 2017
TL;DR: PipeLayer is presented, a ReRAM-based PIM accelerator for CNNs that support both training and testing and proposes highly parallel design based on the notion of parallelism granularity and weight replication, which enables the highly pipelined execution of bothTraining and testing, without introducing the potential stalls in previous work.
Abstract: Convolution neural networks (CNNs) are the heart of deep learning applications. Recent works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access memory (ReRAM) to perform neural computations in memory. We found that training cannot be efficiently supported with the current schemes. First, they do not consider weight update and complex data dependency in training procedure. Second, ISAAC attempts to increase system throughput with a very deep pipeline. It is only beneficial when a large number of consecutive images can be fed into the architecture. In training, the notion of batch (e.g. 64) limits the number of images can be processed consecutively, because the images in the next batch need to be processed based on the updated weights. Third, the deep pipeline in ISAAC is vulnerable to pipeline bubbles and execution stall. In this paper, we present PipeLayer, a ReRAM-based PIM accelerator for CNNs that support both training and testing. We analyze data dependency and weight update in training algorithms and propose efficient pipeline to exploit inter-layer parallelism. To exploit intra-layer parallelism, we propose highly parallel design based on the notion of parallelism granularity and weight replication. With these design choices, PipeLayer enables the highly pipelined execution of both training and testing, without introducing the potential stalls in previous work. The experiment results show that, PipeLayer achieves the speedups of 42.45x compared with GPU platform on average. The average energy saving of PipeLayer compared with GPU implementation is 7.17x.

633 citations