CAD Tool Design for NCL and MTNCL Asynchronous Circuits

This thesis presents an implementation of a method developed to readily convert Boolean designs into an ultra-low power asynchronous design methodology called MTNCL, which combines multi-threshold CMOS (MTCMOS) with NULL Convention Logic (NCL) systems. MTNCL provides the leakage power advantages of an all high-Vt implementation with a reasonable speed penalty compared to the all low-Vt implementation, and has negligible area overhead. The proposed tool utilizes industry-standard CAD tools. This research also presents an Automated Gate-Level Pipelining with Bit-Wise Completion (AGLPBW) method to maximize throughput of delay-insensitive full-word pipelined NCL circuits. These methods have been integrated into the Mentor Graphics and Synopsis CAD tools, using a C-program, which performs the majority of the computations, such that the method can be easily ported to other CAD tool suites. Both methods have been successfully tested on circuits, including a 4-bit × 4-bit multiplier, an unsigned Booth2 multiplier, and a 4-bit/8-operation arithmetic logic unit (ALU

Wavelet analysis of compressed biomedical signals

The paper proposes mathematical apparatus that can be used for wavelet analysis of compressed biomedical signals. As an example of biomedical signals, electrocardiogram and electroencephalogram are considered. A brief description of these signals is given. In the basis of the proposed algorithm of wavelet analysis of compressed biomedical signals lies the use of wavelet decomposition of the signal with the subsequent analysis of approximating coefficients of the set level with the use of continuous wavelet transform and synthesized wavelet. Below is suggested a brief description of the wavelet synthesis procedure for continuous wavelet transform as well as neural network and spline wavelet models proposed by the author. It has been practically proven that application of this algorithm allows us to compress electrocardiogram and electroencephalogram 8 times. In this case possibility to detect the target feature in biomedical signal based on the analysis results of the continuous wavelet transform. Noted, however, that the use of wavelet compression results in a loss of high frequency information in a signal. Therefore, the algorithm must not be applied in cases where the preservation of small fragments in a signal typical of high-frequency components is very important. This algorithm can be applied in the implementation of wavelet analysis of biomedical signals system on mobile devices, where it is important to reduce the amount of stored, transmitted and / or processed information

Enhancing Power Efficient Design Techniques in Deep Submicron Era

Excessive power dissipation has been one of the major bottlenecks for design and manufacture in the past couple of decades. Power efficient design has become more and more challenging when technology scales down to the deep submicron era that features the dominance of leakage, the manufacture variation, the on-chip temperature variation and higher reliability requirements, among others. Most of the computer aided design (CAD) tools and algorithms currently used in industry were developed in the pre deep submicron era and did not consider the new features explicitly and adequately. Recent research advances in deep submicron design, such as the mechanisms of leakage, the source and characterization of manufacture variation, the cause and models of on-chip temperature variation, provide us the opportunity to incorporate these important issues in power efficient design. We explore this opportunity in this dissertation by demonstrating that significant power reduction can be achieved with only minor modification to the existing CAD tools and algorithms. First, we consider peak current, which has become critical for circuit's reliability in deep submicron design. Traditional low power design techniques focus on the reduction of average power. We propose to reduce peak current while keeping the overhead on average power as small as possible. Second, dual Vt technique and gate sizing have been used simultaneously for leakage savings. However, this approach becomes less effective in deep submicron design. We propose to use the newly developed process-induced mechanical stress to enhance its performance. Finally, in deep submicron design, the impact of on-chip temperature variation on leakage and performance becomes more and more significant. We propose a temperature-aware dual Vt approach to alleviate hot spots and achieve further leakage reduction. We also consider this leakage-temperature dependency in the dynamic voltage scaling approach and discover that a commonly accepted result is incorrect for the current technology. We conduct extensive experiments with popular design benchmarks, using the latest industry CAD tools and design libraries. The results show that our proposed enhancements are promising in power saving and are practical to solve the low power design challenges in deep submicron era

CAD Tools for Synthesis of Sleep Convention Logic

This dissertation proposes an automated flow for the Sleep Convention Logic (SCL) asynchronous design style. The proposed flow synthesizes synchronous RTL into an SCL netlist. The flow utilizes commercial design tools, while supplementing missing functionality using custom tools. A method for determining the performance bottleneck in an SCL design is proposed. A constraint-driven method to increase the performance of linear SCL pipelines is proposed. Several enhancements to SCL are proposed, including techniques to reduce the number of registers and total sleep capacitance in an SCL design

Segmentation Of Retinal Blood Vessels Using A Novel Fuzzy Logic Algorithm

In this work, a rule-based method is presented for blood vessel segmentation in digital retinal images. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness. Diagnosis of diabetic retinopathy at an early stage can be done through the segmentation of the blood vessels of retina. Many studies have been carried out in the last decade in order to obtain accurate blood vessel segmentation in retinal images including supervised and rule-based methods. This method uses eight feature vectors for each pixel. These features are means and medians of intensity values of pixel itself, first and second nearest neighbor at four directions. Features are used in fuzzy logic algorithm as crisp input. The final segmentation is obtained using a thresholding method. The method was tested on the publicly available database DRIVE and its results are compared with distinguished published methods. Our method achieved an average accuracy of 93.82% and an area under the receiver operating characteristic curve of 94.19% for DRIVE database. Our results demonstrated an average sensitivity of 72.28% and a specificity of 97.04%. The calculated sensitivity and specificity values for DRIVE database also state that the proposed segmentation method is effective and robust

Complex Neural Networks for Audio

Audio is represented in two mathematically equivalent ways: the real-valued time domain (i.e., waveform) and the complex-valued frequency domain (i.e., spectrum). There are advantages to the frequency-domain representation, e.g., the human auditory system is known to process sound in the frequency-domain. Furthermore, linear time-invariant systems are convolved with sources in the time-domain, whereas they may be factorized in the frequency-domain. Neural networks have become rather useful when applied to audio tasks such as machine listening and audio synthesis, which are related by their dependencies on high quality acoustic models. They ideally encapsulate fine-scale temporal structure, such as that encoded in the phase of frequency-domain audio, yet there are no authoritative deep learning methods for complex audio. This manuscript is dedicated to addressing the shortcoming. Chapter 2 motivates complex networks by their affinity with complex-domain audio, while Chapter 3 contributes methods for building and optimizing complex networks. We show that the naive implementation of Adam optimization is incorrect for complex random variables and show that selection of input and output representation has a significant impact on the performance of a complex network. Experimental results with novel complex neural architectures are provided in the second half of this manuscript. Chapter 4 introduces a complex model for binaural audio source localization. We show that, like humans, the complex model can generalize to different anatomical filters, which is important in the context of machine listening. The complex model\u27s performance is better than that of the real-valued models, as well as real- and complex-valued baselines. Chapter 5 proposes a two-stage method for speech enhancement. In the first stage, a complex-valued stochastic autoencoder projects complex vectors to a discrete space. In the second stage, long-term temporal dependencies are modeled in the discrete space. The autoencoder raises the performance ceiling for state of the art speech enhancement, but the dynamic enhancement model does not outperform other baselines. We discuss areas for improvement and note that the complex Adam optimizer improves training convergence over the naive implementation

Complementary molecular models of learning and memory

The functional capabilities of the brain are formally characterizable in terms of a finite system along with a memory space which it can manipulate. Two types of learning are possible: (1) modification-based learning, associated with alternate realizations of the finite system; (2) memory-based learning, associated with the assimilation, manipulation, and retrieval of memories. Constructive models which fulfill these conditions and which at the same time operate on the basis of molecular information processing principles have certain general features. We describe these features in terms of two interfaced submodels, the first for the finite system and the second for the memory space. The finite system may be realized by networks of neurons in which the specificity of enzyme molecules controls the nerve impulse. Such a realization is amenable to modification-based learning mediated by processes analogous to those of natural evolution and selective theories of antibody synthesis. The memory space is realizable by networks of neurons in which the conformation of dendritic receptor molecules controls the nerve impulse. In this case certain neurons firing in response to an external input undergo sensitization at the dendrites and in such a way that they are loadable and later callable by reference neurons, thereby allowing for reconstruction or manipulation of the firing pattern associated with this input. The overall construction makes a large number of biochemical, anatomical, physiological, and psychological predictions which are either testable or in good agreement with fact.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/21626/1/0000005.pd