179 research outputs found

    Neural Network Methods for Radiation Detectors and Imaging

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    Recent advances in image data processing through machine learning and especially deep neural networks (DNNs) allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration

    Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey

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    In the modern-day era of technology, a paradigm shift has been witnessed in the areas involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Specifically, Deep Neural Networks (DNNs) have emerged as a popular field of interest in most AI applications such as computer vision, image and video processing, robotics, etc. In the context of developed digital technologies and the availability of authentic data and data handling infrastructure, DNNs have been a credible choice for solving more complex real-life problems. The performance and accuracy of a DNN is a way better than human intelligence in certain situations. However, it is noteworthy that the DNN is computationally too cumbersome in terms of the resources and time to handle these computations. Furthermore, general-purpose architectures like CPUs have issues in handling such computationally intensive algorithms. Therefore, a lot of interest and efforts have been invested by the research fraternity in specialized hardware architectures such as Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), and Coarse Grained Reconfigurable Array (CGRA) in the context of effective implementation of computationally intensive algorithms. This paper brings forward the various research works carried out on the development and deployment of DNNs using the aforementioned specialized hardware architectures and embedded AI accelerators. The review discusses the detailed description of the specialized hardware-based accelerators used in the training and/or inference of DNN. A comparative study based on factors like power, area, and throughput, is also made on the various accelerators discussed. Finally, future research and development directions are discussed, such as future trends in DNN implementation on specialized hardware accelerators. This review article is intended to serve as a guide for hardware architectures for accelerating and improving the effectiveness of deep learning research.publishedVersio

    CNN2Gate: an implementation of convolutional neural networks inference on FPGAs with automated design space exploration

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    ABSTRACT: Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement CNNs are increasing rapidly. This is due to the lower power consumption and easy reconfigurability that are offered by these platforms. Because of the research efforts put into topics, such as architecture, synthesis, and optimization, some new challenges are arising for integrating suitable hardware solutions to high-level machine learning software libraries. This paper introduces an integrated framework (CNN2Gate), which supports compilation of a CNN model for an FPGA target. CNN2Gate is capable of parsing CNN models from several popular high-level machine learning libraries, such as Keras, Pytorch, Caffe2, etc. CNN2Gate extracts computation flow of layers, in addition to weights and biases, and applies a “given” fixed-point quantization. Furthermore, it writes this information in the proper format for the FPGA vendor’s OpenCL synthesis tools that are then used to build and run the project on FPGA. CNN2Gate performs design-space exploration and fits the design on different FPGAs with limited logic resources automatically. This paper reports results of automatic synthesis and design-space exploration of AlexNet and VGG-16 on various Intel FPGA platforms

    Memory and information processing in neuromorphic systems

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    A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

    Deep learning in crowd counting: A survey

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    Counting high-density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three-tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small-scale, large-scale and hyper-scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data-driven perspective: multi-scale networks, single-column networks, multi-column networks, multi-task networks, attention networks and weak-supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three-tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small-scale datasets has slowed down. There are few new datasets and algorithms on small-scale datasets. The studies focused on large or hyper-scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real-world scenarios and processing large crowds in real-time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research.BHF, AA/18/3/34220Hope Foundation for Cancer Research, RM60G0680GCRF, P202PF11;Sino‐UK Industrial Fund, RP202G0289LIAS, P202ED10, P202RE969Data Science Enhancement Fund, P202RE237Sino‐UK Education Fund, OP202006Fight for Sight, 24NN201Royal Society International Exchanges Cost Share Award, RP202G0230MRC, MC_PC_17171BBSRC, RM32G0178B
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