A content-addressable memory compares an input search word against all rows of stored words in an array in a highly parallel manner. While supplying a very powerful functionality for many ...applications in pattern matching and search, it suffers from large area, cost and power consumption, limiting its use. Past improvements have been realized by using memristors to replace the static random-access memory cell in conventional designs, but employ similar schemes based only on binary or ternary states for storage and search. We propose a new analog content-addressable memory concept and circuit to overcome these limitations by utilizing the analog conductance tunability of memristors. Our analog content-addressable memory stores data within the programmable conductance and can take as input either analog or digital search values. Experimental demonstrations, scaled simulations and analysis show that our analog content-addressable memory can reduce area and power consumption, which enables the acceleration of existing applications, but also new computing application areas.
Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be ...demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.
We report sub-nanosecond switching of a metal-oxide-metal memristor utilizing a broadband 20 GHz experimental setup developed to observe fast switching dynamics. Set and reset operations were ...successfully performed in the tantalum oxide memristor using pulses with durations of 105 and 120 ps, respectively. Reproducibility of the sub-nanosecond switching was also confirmed as the device switched over consecutive cycles.
High-Speed and Low-Energy Nitride Memristors Choi, Byung Joon; Torrezan, Antonio C.; Strachan, John Paul ...
Advanced functional materials,
August 2, 2016, Letnik:
26, Številka:
29
Journal Article
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High‐performance memristors based on AlN films have been demonstrated, which exhibit ultrafast ON/OFF switching times (≈85 ps for microdevices with waveguide) and relatively low switching current ...(≈15 μA for 50 nm devices). Physical characterizations are carried out to understand the device switching mechanism, and rationalize speed and energy performance. The formation of an Al‐rich conduction channel through the AlN layer is revealed. The motion of positively charged nitrogen vacancies is likely responsible for the observed switching.
Ultrafast switching of an AlN memristor: ON switching is acheived using an 85 ps positive voltage pulse, and OFF switching using an 85 ps negative voltage pulse on the Al electrode of a Pt/AlN/Al memristor stack. A relatively low switching current (≈15 μA for 50 nm devices) has also been demonstrated in these memristors based on AlN films. The formation of an Al‐rich conduction channel through the AlN layer is revealed.
Joule‐heating induced conductance‐switching is studied in VO2, a Mott insulator. Complementary in situ techniques including optical characterization, blackbody microscopy, scanning transmission X‐ray ...microscopy (STXM) and numerical simulations are used. Abrupt redistribution in local temperature is shown to occur upon conductance‐switching along with a structural phase transition, at the same current.
Abstract
Negative differential resistance behavior in oxide memristors, especially those using NbO
2
, is gaining renewed interest because of its potential utility in neuromorphic computing. However, ...there has been a decade-long controversy over whether the negative differential resistance is caused by a relatively low-temperature non-linear transport mechanism or a high-temperature Mott transition. Resolving this issue will enable consistent and robust predictive modeling of this phenomenon for different applications. Here we examine NbO
2
memristors that exhibit both a current-controlled and a temperature-controlled negative differential resistance. Through thermal and chemical spectromicroscopy and numerical simulations, we confirm that the former is caused by a ~400 K non-linear-transport-driven instability and the latter is caused by the ~1000 K Mott metal-insulator transition, for which the thermal conductance counter-intuitively decreases in the metallic state relative to the insulating state.
Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building ...blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with "leaky integrate-and-fire" dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals.
Abstract
Tree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer improved ...interpretability compared to Deep Neural Networks (DNN). However, these models are difficult to optimize for fast inference at scale without accuracy loss in von Neumann architectures due to non-uniform memory access patterns. Recently, we proposed a novel analog content addressable memory (CAM) based on emerging memristor devices for fast look-up table operations. Here, we propose for the first time to use the analog CAM as an in-memory computational primitive to accelerate tree-based model inference. We demonstrate an efficient mapping algorithm leveraging the new analog CAM capabilities such that each root to leaf path of a Decision Tree is programmed into a row. This new in-memory compute concept for enables few-cycle model inference, dramatically increasing 10
3
× the throughput over conventional approaches.
A key requirement for using memristors in circuits is a predictive model for device behavior that can be used in simulations and to guide designs. We analyze one of the most promising materials, ...tantalum oxide, for high density, low power, and high-speed memory. We perform an ensemble of measurements, including time dynamics across nine decades, to deduce the underlying state equations describing the switching in Pt/TaO x /Ta memristors. A predictive, compact model is found in good agreement with the measured data. The resulting model, compatible with SPICE, is then used to understand trends in terms of switching times and energy consumption, which in turn are important for choosing device operating points and handling interactions with other circuit elements.
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