A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing

: Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving

One-step regression and classification with cross-point resistive memory arrays

Machine learning has been getting attention in recent years as a tool to process big data generated by the ubiquitous sensors used in daily life. High-speed, low-energy computing machines are in demand to enable real-time artificial intelligence processing of such data. These requirements challenge the current metal-oxide-semiconductor technology, which is limited by Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures, and devices are thus strongly needed to accelerate data-intensive applications. Here, we show that a cross-point resistive memory circuit with feedback configuration can train traditional machine learning algorithms such as linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. One-step learning is further supported by simulations of the prediction of housing price in Boston and the training of a two-layer neural network for MNIST digit recognition

Impact of oxide and electrode materials on the switching characteristics of oxide ReRAM devices

Resistive switching random-access memory (ReRAM) is one of the most promising technologies for non-volatile memories. Thanks to the low power and high speed operation, the high density CMOS-compatible integration, and the high cycling endurance, the ReRAM technology is becoming a strong candidate for high-density storage arrays and novel in-memory computing systems. However, ReRAM suffers from cycle-to-cycle switching variability and noise-induced resistance fluctuations, leading to insufficient read margin between the programmed resistive states. To overcome the existing challenges, a deep understanding of the roles of the ReRAM materials in the device characteristics is essential. To better understand the role of the switching layer material in controlling ReRAM performance and reliability, this work compares SiO x - and HfO 2 -based ReRAM at fixed geometry and electrode materials. Ti/HfO 2 /C and Ti/SiO x /C devices are compared from the point of view of the forming process, switching characteristics, resistance variability, and temperature stability of the programmed states. The results show clear similarities for the two different oxides, including a similar resistance window and stability at high temperatures, thus suggesting a common nature of the switching mechanism, highlighting the importance of the electrodes. On the other hand, the oxide materials play a clear role in the forming, breakdown, and variability characteristics. The discrimination between the role of the oxide and the electrode materials in the ReRAM allows ReRAM optimization via materials engineering to be better explored for future memory and computing applications

Mixing characteristics of cracked gaseous hydrocarbon fuels in a scramjet combustor

High-performance hydrocarbon-fuelled scramjet engines require efficient fuel-air mixing due to the relatively short flow residence time through the combustor. At high temperatures, hydrocarbon fuels react endothermically and absorb thermal energy from the surroundings. The process known as cracking becomes essential at high Mach numbers to increase the total heat-sink capacity of the fuel. This study presents the results of chemically frozen numerical simulations that investigate the mixing characteristics of cracked gaseous heavy hydrocarbon fuels injected through a circular, flush-wall porthole injector. The mixing characteristics of fuel compositions representing cracking efficiencies ranging from 0 to 100% are investigated. The mixing rates and flow structures are found to change with fuel compositions. As the cracking increases, the mixing and streamwise circulation increase for an injectant. However, the jet penetration and stagnation pressure losses decrease. The streamwise circulation is found to have a strong influence on the mixing, the injection pressure on the jet penetration and the strength of the bow shock on stagnation pressure losses. Overall, it is shown that there are mixing benefits to be gained by injecting cracked hydrocarbon fuels compared to heavy uncracked fuels in scramjets

In-memory PageRank using a Crosspoint Array of Resistive Switching Memory (RRAM) devices

Thanks to the high parallelism endowed by physical rules, in-memory computing with crosspoint resistive memory arrays has been applied to accelerate typical dataintensive tasks such as the training and inference of deep learning. Recently, it has been shown that a crosspoint resistive switching memory (RRAM) circuit with a feedback configuration can be used to solve linear systems, compute eigenvectors, and rank webpages in just one step. Here, we demonstrate the PageRank with a real database (the Harvard500) together with an 8-level RRAM model that is based on experimental measurements, including the max/min conductance ratio, the high conductance range and the standard deviation of each level. By using a verify algorithm for the RRAM device programming, the PageRank result from the crosspoint circuit shows a cosine similarity of 93.5% with respect to the floating-point solution. With more discrete conductance levels and a broader high conductance range in the RRAM model, a better performance of cosine similarity up to 97% can be achieved. This work supports the feasibility of in-memory PageRank with realistic RRAM devices for real-world networks

Combining accuracy and plasticity in convolutional neural networks based on resistive memory arrays for autonomous learning

Nowadays, artificial neural networks (ANNs) can outperform the human brain ability in specific tasks. However, ANNs cannot replicate the efficient and low-power learning, adaptation and consolidation typical of biological organisms. Here, we present a hardware design, based on arrays of SiOx resistive switching memories (RRAMs), that allows to combine the accuracy of convolutional neural networks with the flexibility of bio-inspired neuronal plasticity. In order to enable the combination of the stable and the plastic attributes of the network, we exploit the spike-frequency adaptation of the neurons relying on the multilevel programming of the RRAM devices. This procedure enhances the efficiency and accuracy of the network for MNIST, noisy MNIST (N-MNIST), Fashion-MNIST and CIFAR-10 datasets, with inference accuracies of about 99% to 89%, respectively. We also demonstrate that the hardware is capable of asynchronous self-adaptation of its operative frequency according to the fire rate of the spiking neuron, thus optimizing the whole behavior of the network. We finally show that the system enables fast and accurate filter re-training to overcome catastrophic forgetting, showing high efficiency in terms of operations per second and robustness against device non-idealities. This work paves the way for the theoretical modelling and hardware realization of resilient autonomous systems in dynamic environments