891 research outputs found
A mean field neural network for hierarchical module placement
This paper proposes a mean field neural network for the two-dimensional module placement problem. An efficient coding scheme with only O(N log N) neurons is employed where N is the number of modules. The neurons are evolved in groups of N in log N iteration steps such that the circuit is recursively partitioned in alternating vertical and horizontal directions. In our simulations, the network was able to find optimal solutions to all test problems with up to 128 modules
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A digital neural network approach to speech recognition
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents two novel methods for isolated word speech recognition based on sub-word components. A digital neural network is the fundamental processing strategy in both methods. The first design is based on the 'Separate Segmentation &
Labelling' (SS&L) approach. The spectral data of the input utterance is first segmented into phoneme-like units which are then time normalised by linear time normalisation. The neural network labels the
time-normalised phoneme-like segments 78.36% recognition accuracy is achieved for the phoneme-like unit. In the second design, no time normalisation is required. After segmentation, recognition is performed by classifying the data in a window as it is slid one frame at a time, from the start to the end of of each phoneme-like segment in the utterance. 73.97% recognition accuracy for the phoneme-like unit is achieved in this application. The parameters of the neural net have been optimised for
maximum recognition performance. A segmentation strategy using the sum of the difference in filterbank channel energy over successive spectra produced 80.27% correct segmentation of isolated utterances into phoneme-like units. A linguistic processor based on that of Kashyap & Mittal [84] enables 93.11% and 93.49% word recognition accuracy to be achieved for the SS&L and 'Sliding Window' recognisers respectively. The linguistic processor has been redesigned to make it portable so that it can be easily applied to any phoneme based isolated word speech recogniser.This work is funded by the Ministry of Science & Technology, Government of Pakistan
The Fifth NASA Symposium on VLSI Design
The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design
Research on performance enhancement for electromagnetic analysis and power analysis in cryptographic LSI
制度:新 ; 報告番号:甲3785号 ; 学位の種類:博士(工学) ; 授与年月日:2012/11/19 ; 早大学位記番号:新6161Waseda Universit
GRAPH-BASED APPROACHES FOR IMBALANCED DATA IN FUNCTIONAL GENOMICS
The Gene Function Prediction (GFP) problem consists in inferring biological properties for the genes whose function is unknown or only partially
known, and raises challenging issues from both a machine learning and a
computational biology standpoint.
The GFP problem can be formalized as a semi-supervised learning problem in an undirected graph. Indeed, given a graph with a partial graph labeling, where nodes represent genes, edges functional relationships between
genes, and labels their membership to functional classes, GFP consists in
inferring the unknown functional classes of genes, by exploiting the topological relationships of the networks and the available a priori knowledge about
the functional properties of genes.
Several network-based machine learning algorithms have been proposed
for solving this problem, including Hopfield networks and label propagation
methods; however, some issues have been only partially considered, e.g. the
preservation of the prior knowledge and the unbalance between positive and
negative labels.
A first contribution of the thesis is the design of a Hopfield-based cost
sensitive neural network algorithm (COSNet) to address these learning issues. The method factorizes the solution of the problem in two parts: 1) the
subnetwork composed by the labelled vertices is considered, and the network
parameters are estimated through a supervised algorithm; 2) the estimated
parameters are extended to the subnetwork composed of the unlabeled vertices, and the attractor reached by the dynamics of this subnetwork allows
to predict the labeling of the unlabeled vertices.
The proposed method embeds in the neural algorithm the \u201ca priori\u201d
knowledge coded in the labeled part of the graph, and separates node labels
and neuron states, allowing to differentially weight positive and negative
node labels, and to perform a learning approach that takes into account the
\u201cunbalance problem\u201d that affects GFP.
A second contribution of this thesis is the development of a new algorithm (LSI ) which exploits some ideas of COSNet for evaluating the predictive capability of each input network. By this algorithm we can estimate the
effectiveness of each source of data for predicting a specific class, and then
we can use this information to appropriately integrate multiple networks by
weighting them according to an appropriate integration scheme.
Both COSNet and LSI are computationally efficient and scale well with
the dimension of the data.
COSNet and LSI have been applied to the genome-wide prediction of gene functions in the yeast and mouse model organisms, achieving results
comparable with those obtained with state-of-the-art semi-supervised and
supervised machine learning methods
A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction
Single-image 3D reconstruction is a research challenge focused on predicting
3D object shapes from single-view images. This task requires significant data
acquisition to predict both visible and occluded portions of the shape.
Furthermore, learning-based methods face the difficulty of creating a
comprehensive training dataset for all possible classes. To this end, we
propose a continual learning-based 3D reconstruction method where our goal is
to design a model using Variational Priors that can still reconstruct the
previously seen classes reasonably even after training on new classes.
Variational Priors represent abstract shapes and combat forgetting, whereas
saliency maps preserve object attributes with less memory usage. This is vital
due to resource constraints in storing extensive training data. Additionally,
we introduce saliency map-based experience replay to capture global and
distinct object features. Thorough experiments show competitive results
compared to established methods, both quantitatively and qualitatively.Comment: 15 page
AI/ML Algorithms and Applications in VLSI Design and Technology
An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations
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