CMOS and memristive hardware for neuromorphic computing

The ever-increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low power, high speed, and noise-tolerant computing capabilities of the brain, may provide such a shift. To that end, various aspects of the brain, from its basic building blocks, such as neurons and synapses, to its massively parallel in-memory computing networks have been being studied by the huge neuroscience community. Concurrently, many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop bio-inspired (neuromorphic) computing platforms

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

Neuromorphic Dynamics at the Nanoscale in Silicon Suboxide RRAM

Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic applications due to their scalability, low-power operation and diverse functional behaviors. However, understanding the dynamics of individual filaments, and the surrounding material, is challenging, owing to the typically very large cross-sectional areas of test devices relative to the nanometer scale of individual filaments. In the present work, conductive atomic force microscopy is used to study the evolution of conductivity at the nanoscale in a fully CMOS-compatible silicon suboxide thin film. Distinct filamentary plasticity and background conductivity enhancement are reported, suggesting that device behavior might be best described by composite core (filament) and shell (background conductivity) dynamics. Furthermore, constant current measurements demonstrate an interplay between filament formation and rupture, resulting in current-controlled voltage spiking in nanoscale regions, with an estimated optimal energy consumption of 25 attojoules per spike. This is very promising for extremely low-power neuromorphic computation and suggests that the dynamic behavior observed in larger devices should persist and improve as dimensions are scaled down

MoS2 based Memristive Synapses for Neuromorphic Computing

Brain inspired computing enabled by memristors have gained prominence over the years due to its nano-scale footprint, reduced complexity for implementing synapses and neurons. Several demonstrations show two-dimensional (2D) materials as a promising platform for realization of robust and energy-efficient memristive synapses. Ideally, a synapse should exhibit low cycle-to-cycle (C-C) and device-to-device (D-D) variability along with high maximum /minimum conductance (Gmax/Gmin) ratio, linearity and symmetry in weight update for obtaining high learning accuracy in neural networks (NNs). However, the demonstration of neuromorphic circuits using conventional materials systems has been limited by high C-C and D-D variability and non-linearity in the weight updates. In this study, we have realized robust memristive synapses using 2D molybdenum disulfide (MoS2) to address the concerns like high variability and non-linear weight update and asymmetry. We have utilized engineering techniques like electrode and stack engineering to realize ultra-low variability and linear weight update in MoS2 synapses. The ultra-low C-C and D-D variability in SET voltage, RESET power and weight update is demonstrated in Au/MoS2/Ti/Au synapses. Further, these synapses were integrated with MoS2 leaky-integrate and fire (LIF) neurons to realize AND, OR and NOT logic gates proving the viability of these synapses for in-memory computing. However, these MoS2 synapses suffer from low Gmax/Gmin ratio. We have employed stack engineering to increase Gmax/Gmin ratio while preserving low variability. In that regard, the active medium is modified to a heterogenous stack of MoS2/SiOx with Ti/Au bottom and top electrodes. We observe an increase in the Gmax/Gmin ratio from 2 to ~10. Further, electrode engineering is used to realize graphene/MoS2/SiOx/Ni to obtain linear weight update with identical pulses essential for online training of NNs. This work substantiates the necessity of engineering techniques to implement essential synaptic characteristics like ultra-low variability and linear and symmetric weight update

Synaptic Behavior in Metal Oxide-Based Memristors

With the end of Moore’s law in sight, new computing paradigms are needed to fulfill the increasing demands on data and processing potentials. Inspired by the operation of the human brain, from the dimensionality, energy and underlying functionalities, neuromorphic computing systems that are building upon circuit elements to mimic the neurobiological activities are good concepts to meet the challenge. As an important factor in a neuromorphic computer, electronic synapse has been intensively studied. The utilization of transistors, atomic switches and memristors has been proposed to perform synaptic functions. Memristors, with several unique properties, are exceptional candidates for emulating artificial synapses and thus for building artificial neural networks. In this paper, metal oxide-based memristor synapses are reviewed, from materials, properties, mechanisms, to architecture. The synaptic plasticity and learning rules are described. The electrical switching characteristics of a variety of metal oxide-based memristors are discussed, with a focus on their application as biological synapses

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing