12 research outputs found
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
The memristive crossbar aims to implement analog weighted neural network,
however, the realistic implementation of such crossbar arrays is not possible
due to limited switching states of memristive devices. In this work, we propose
the design of an analog deep neural network with binary weight update through
backpropagation algorithm using binary state memristive devices. We show that
such networks can be successfully used for image processing task and has the
advantage of lower power consumption and small on-chip area in comparison with
digital counterparts. The proposed network was benchmarked for MNIST
handwritten digits recognition achieving an accuracy of approximately 90%
Introduction to Memristive HTM Circuits
Hierarchical temporal memory (HTM) is a cognitive learning algorithm intended to mimic the working principles of neocortex, part of the human brain said to be responsible for data classification, learning, and making predictions. Based on the combination of various concepts of neuroscience, it has already been shown that the software realization of HTM is effective on different recognition, detection, and prediction making tasks. However, its distinctive features, expressed in terms of hierarchy, modularity, and sparsity, suggest that hardware realization of HTM can be attractive in terms of providing faster processing speed as well as small memory requirements, on-chip area, and total power consumption. Despite there are few works done on hardware realization for HTM, there are promising results which illustrate effectiveness of incorporating an emerging memristor device technology to solve this open-research problem. Hence, this chapter reviews hardware designs for HTM with specific focus on memristive HTM circuits
Application of Cortical Learning Algorithms to Movement Classification
Classifying the objects’ trajectories extracted from Closed-Circuit Television (CCTV) feeds is a key video analytic module to systematize or rather help to automate both the real-time monitoring and the video forensic process. Machine learning algorithms have been heavily proposed to solve the problem of movement classification. However, they still suffer from various limitations such as their limited ability to cope with multi-dimensional data streams or data with temporal behaviour. Recently, the Hierarchical Temporal Memory (HTM) and its implementation, the Cortical Learning Algorithms (CLA) have proven their success to detect temporal anomalies from a noisy data stream. In this paper, a novel CLA-based movement classification algorithm has been proposed and devised to detect abnormal movements in realistic video surveillance scenarios. Tests applied on twenty-three videos have been conducted and the proposed algorithm has been evaluated and compared against several state-of-the-art anomaly detection algorithms. Our algorithm has achieved 66.29% average F-measure, with an improvement of 15.5% compared to the k-Nearest Neighbour Global Anomaly Score (kNN-GAS) algorithm. The Independent Component Analysis-Local Outlier Probability (ICA-LoOP) scored 42.75%, the Singular Value Decomposition Influence Outlier (SVD-IO) achieved 34.82%, whilst the Connectivity Based Factor algorithm (CBOF) scored 8.72%. The proposed models have empirically portrayed positive potential and had exceeded in performance when compared to state-of-the-art algorithms
Temporal - spatial recognizer for multi-label data
Pattern recognition is an important artificial intelligence task with practical applications in many fields such as medical and species distribution. Such application involves overlapping data points which are demonstrated in the multi- label dataset. Hence, there is a need for a recognition algorithm that can separate the overlapping data points in order to recognize the correct pattern. Existing recognition methods suffer from sensitivity to noise and overlapping points as they could not recognize a pattern when there is a shift in the position of the data points. Furthermore, the methods do not implicate temporal information in the process of recognition, which leads to low quality of data clustering. In this study, an improved pattern recognition method based on Hierarchical Temporal Memory (HTM) is proposed to solve the overlapping in data points of multi- label dataset. The imHTM (Improved HTM) method includes improvement in two of its components; feature extraction and data clustering. The first improvement is realized as TS-Layer Neocognitron algorithm which solves the shift in position problem in feature extraction phase. On the other hand, the data clustering step, has two improvements, TFCM and cFCM (TFCM with limit- Chebyshev distance metric) that allows the overlapped data points which occur in patterns to be separated correctly into the relevant clusters by temporal clustering. Experiments on five datasets were conducted to compare the proposed method (imHTM) against statistical, template and structural pattern recognition methods. The results showed that the percentage of success in recognition accuracy is 99% as compared with the template matching method (Featured-Based Approach, Area-Based Approach), statistical method (Principal Component Analysis, Linear Discriminant Analysis, Support Vector Machines and Neural Network) and structural method (original HTM). The findings indicate that the improved HTM can give an optimum pattern recognition accuracy, especially the ones in multi- label dataset
Hierarchical Temporal Memory using Memristor Networks: A Survey
This paper presents a survey of the currently available hardware designs for
implementation of the human cortex inspired algorithm, Hierarchical Temporal
Memory (HTM). In this review, we focus on the state of the art advances of
memristive HTM implementation and related HTM applications. With the advent of
edge computing, HTM can be a potential algorithm to implement on-chip near
sensor data processing. The comparison of analog memristive circuit
implementations with the digital and mixed-signal solutions are provided. The
advantages of memristive HTM over digital implementations against performance
metrics such as processing speed, reduced on-chip area and power dissipation
are discussed. The limitations and open problems concerning the memristive HTM,
such as the design scalability, sneak currents, leakage, parasitic effects,
lack of the analog learning circuits implementations and unreliability of the
memristive devices integrated with CMOS circuits are also discussed
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