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

Design of CMOS-memristor Circuits for LSTM architecture

Long Short-Term memory (LSTM) architecture is a well-known approach for building recurrent neural networks (RNN) useful in sequential processing of data in application to natural language processing. The near-sensor hardware implementation of LSTM is challenged due to large parallelism and complexity. We propose a 0.18 m CMOS, GST memristor LSTM hardware architecture for near-sensor processing. The proposed system is validated in a forecasting problem based on Keras model

LSTM neural network implementation using memristive crossbar circuits and its various topologies

Neural Network (NN) algorithms have existed for long time now. However, they started to reemerge only after computers had been invented, because computational resources are required to implement NN algorithms. In fact, computers themselves are not fast enough to train and run the NNs. It can take days to train some complex neural networks for certain applications. One of the complex NNs that became widely used is Long-Short Term Memory (LSTM) NN algorithm. As a broader approach to increase the computation speed and decrease power consumption of neural network algorithms, hardware realizations of the neural networks have emerged. Mainly FPGA and analog hardware are used for these purposes. On this occasion, it happens to be only FPGA implementations of LSTM exist. Using this lack, this thesis work mainly aims to show that LSTM neural network is realizable and functional in analog hardware. In fact, analog hardware using memristive crossbars can be a potential solution to the speed bottleneck experienced in software implementations of LSTM and other complex neural networks in general. This work mainly focuses on implementation of already trained LSTM neural networks in analog circuitry. Since training consists of both forward and backward pass computations through NNs, first, there should be focus on implementing the circuitry that can run forward passes. This forward running circuit further can be extended to a complete circuit which would include training circuitry. Additionally, there exists various LSTM topologies. Software analysis has been done to compare the performance of each LSTM architecture for time-series prediction and time-series classification applications. Each of the architectures can be implemented in analog circuitry without great difficulty using voltage-based LSTM circuit parts due its easiness to reconfigure. Fully functional implementation of the voltage-based memristive LSTM in SPICE circuit simulator is the main contribution of this thesis work. In comparison, current-based LSTM circuit parts may not be easily rearranged due to the difficulty of passing currents from one stage to the next without degradation in magnitude

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies ranging from emerging memristive devices, to established Field Programmable Gate Arrays (FPGAs), and mature Complementary Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. After providing the required background, we unify the sparsely distributed research on neural network and neuromorphic hardware implementations as applied to the healthcare domain. In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that different accelerators and neuromorphic processors introduce to healthcare and biomedical domains. This paper can serve a large audience, ranging from nanoelectronics researchers, to biomedical and healthcare practitioners in grasping the fundamental interplay between hardware, algorithms, and clinical adoption of these tools, as we shed light on the future of deep networks and spiking neuromorphic processing systems as proponents for driving biomedical circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21 pages, 10 figures, 5 tables

Memristor-based LSTM neuromorphic circuits for offshore wind turbine blade fault detection

The UK offshore wind industry is rapidly growing to meet CO2 emission targets. However, the main drawback of the offshore environment is the increased cost of maintenance. Artificial Neural Networks (ANN) show great potential to reduce this cost. Long Short-Term Memory (LSTM) is a form of Recurrent Neural Network (RNN) that shows promising results in solving time series-based problems, making them ideally suited for wind turbine condition monitoring. A dedicated circuit for a LSTM-based ANN that uses memristors will allow for more power efficient and faster computation, whilst being able to overcome the von Neumann bottleneck

Nonvolatile CMOS memristor, reconfigurable array and its application in power load forecasting

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/TII.2023.3341256The high cost, low yield, and low stability of nano-materials significantly hinder the application and development of memristors. To promote the application of memristors, researchers proposed a variety of memristor emulators to simulate memristor functions and apply them in various fields. However these emulators lack nonvolatile characteristics, limiting their scope of application. This paper proposes an innovative nonvolatile memristor circuit based on complementary metal-oxide-semiconductor (CMOS) technology, expanding the horizons of memristor emulators. The proposed memristor is fabricated in a reconfigurable array architecture using the standard CMOS process, allowing the connection between memristors to be altered by configuring the on-off state of switches. Compared to nano-material memristors, the CMOS nonvolatile memristor circuit proposed in this paper offers advantages of low manufacturing cost and easy mass production, which can promote the application of memristors. The application of the reconfigurable array is further studied by constructing an Echo State Network (ESN) for short-term load forecasting in the power system.Peer reviewe