Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

Biologically-inspired neuromorphic computing paradigms are computational platforms that imitate synaptic and neuronal activities in the human brain to process big data flows in an efficient and cognitive manner. In the past decades, neuromorphic computing has been widely investigated in various application fields such as language translation, image recognition, modeling of phase, and speech recognition, especially in neural networks (NNs) by utilizing emerging nanotechnologies; due to their inherent miniaturization with low power cost, they can alleviate the technical barriers of neuromorphic computing by exploiting traditional silicon technology in practical applications. In this work, we review recent advances in the development of brain-inspired computing (BIC) systems with respect to the perspective of a system designer, from the device technology level and circuit level up to the architecture and system levels. In particular, we sort out the NN architecture determined by the data structures centered on big data flows in application scenarios. Finally, the interactions between the system level with the architecture level and circuit/device level are discussed. Consequently, this review can serve the future development and opportunities of the BIC system design

Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Doctor of Philosophy

dissertationDeep Neural Networks (DNNs) are the state-of-art solution in a growing number of tasks including computer vision, speech recognition, and genomics. However, DNNs are computationally expensive as they are carefully trained to extract and abstract features from raw data using multiple layers of neurons with millions of parameters. In this dissertation, we primarily focus on inference, e.g., using a DNN to classify an input image. This is an operation that will be repeatedly performed on billions of devices in the datacenter, in self-driving cars, in drones, etc. We observe that DNNs spend a vast majority of their runtime to runtime performing matrix-by-vector multiplications (MVM). MVMs have two major bottlenecks: fetching the matrix and performing sum-of-product operations. To address these bottlenecks, we use in-situ computing, where the matrix is stored in programmable resistor arrays, called crossbars, and sum-of-product operations are performed using analog computing. In this dissertation, we propose two hardware units, ISAAC and Newton.In ISAAC, we show that in-situ computing designs can outperform DNN digital accelerators, if they leverage pipelining, smart encodings, and can distribute a computation in time and space, within crossbars, and across crossbars. In the ISAAC design, roughly half the chip area/power can be attributed to the analog-to-digital conversion (ADC), i.e., it remains the key design challenge in mixed-signal accelerators for deep networks. In spite of the ADC bottleneck, ISAAC is able to out-perform the computational efficiency of the state-of-the-art design (DaDianNao) by 8x. In Newton, we take advantage of a number of techniques to address ADC inefficiency. These techniques exploit matrix transformations, heterogeneity, and smart mapping of computation to the analog substrate. We show that Newton can increase the efficiency of in-situ computing by an additional 2x. Finally, we show that in-situ computing, unfortunately, cannot be easily adapted to handle training of deep networks, i.e., it is only suitable for inference of already-trained networks. By improving the efficiency of DNN inference with ISAAC and Newton, we move closer to low-cost deep learning that in turn will have societal impact through self-driving cars, assistive systems for the disabled, and precision medicine

EFFICIENTLY ACCELERATING SPARSE PROBLEMS BY ENABLING STREAM ACCESSES TO MEMORY USING HARDWARE/SOFTWARE TECHNIQUES

The objective of this research is to improve the performance of sparse problems that have a wide range of applications but still, suffer from serious challenges when running on modern computers. In summary, the challenges include the underutilization of available memory bandwidth because of lack of spatial locality, dependencies in computation, or slow mechanisms for decompressing the sparse data, and the underutilization of concurrent compute engines because of the distribution of non-zero values in sparse data. Our key insight to address the aforementioned challenges is that based on the type of the problem, we either use an intelligent reduction tree near memory to process data while gathering them from random locations of memory, transform the computations mathematically to extract more parallelism, modify the distribution of non-zero elements, or change the representation of sparse data. By applying such techniques, the execution adapts more effectively to given hardware resources. To this end, this research introduces hardware/software techniques to enable stream accesses to memory for accelerating four main categories of sparse problems including the inference of recommendation systems, iterative solvers of partial differential equations (PDEs), deep neural networks (DNNs), and graph algorithms.Ph.D

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas