Fault Tolerance of Stochastic Decoders for Error Correcting Codes

Low-density Parity-check (LDPC) codes are very powerful linear error-correcting codes, first introduced by Gallager in 1963. They are now used in many communication standards due to their ability to achieve near Shannon-capacity performance. Stochastic decoding is a hardware-efficient method of iterative decoding of LDPC codes. In this work, we investigate the capability of stochastic decoding to tolerate circuit soft errors while maintaining good bit error rate performance and low error floor. Soft errors can be intended faults as a result of either supply voltage scaling to reduce power consumption or overclocking the system to achieve a higher throughput. They can also be unintended faults as a result of temperature or process variations. We develop two models to emulate these circuit errors at the system level. We apply our models to two standardized LDPC codes (10GBASE-T and WiMAX). Simulation results show that stochastic decoding is very tolerant to faults and errors, where it can tolerate a probability of setup time violation of 0.1 in the wires of the decoder. Hence, stochastic decoding can be very useful in systems with very low power or high performance requirements where we can push the limits of power or speed by lowering the supply voltage or highly overclocking the system while maintaining good performance. In addition, a chip has been designed and sent to fabrication to do post-silicon validation and verify our models

The hardware implementation of an artificial neural network using stochastic pulse rate encoding principles

In this thesis the development of a hardware artificial neuron device and artificial neural network using stochastic pulse rate encoding principles is considered. After a review of neural network architectures and algorithmic approaches suitable for hardware implementation, a critical review of hardware techniques which have been considered in analogue and digital systems is presented. New results are presented demonstrating the potential of two learning schemes which adapt by the use of a single reinforcement signal. The techniques for computation using stochastic pulse rate encoding are presented and extended with new novel circuits relevant to the hardware implementation of an artificial neural network. The generation of random numbers is the key to the encoding of data into the stochastic pulse rate domain. The formation of random numbers and multiple random bit sequences from a single PRBS generator have been investigated. Two techniques, Simulated Annealing and Genetic Algorithms, have been applied successfully to the problem of optimising the configuration of a PRBS random number generator for the formation of multiple random bit sequences and hence random numbers. A complete hardware design for an artificial neuron using stochastic pulse rate encoded signals has been described, designed, simulated, fabricated and tested before configuration of the device into a network to perform simple test problems. The implementation has shown that the processing elements of the artificial neuron are small and simple, but that there can be a significant overhead for the encoding of information into the stochastic pulse rate domain. The stochastic artificial neuron has the capability of on-line weight adaption. The implementation of reinforcement schemes using the stochastic neuron as a basic element are discussed

Measurements, Models, Systems and Design

531 s.

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method

Physics of Ionic Conduction in Narrow Biological and Artificial Channels

The book reprints a set of important scientific papers applying physics and mathematics to address the problem of selective ionic conduction in narrow water-filled channels and pores. It is a long-standing problem, and an extremely important one. Life in all its forms depends on ion channels and, furthermore, the technological applications of artificial ion channels are already widespread and growing rapidly. They include desalination, DNA sequencing, energy harvesting, molecular sensors, fuel cells, batteries, personalised medicine, and drug design. Further applications are to be anticipated.The book will be helpful to researchers and technologists already working in the area, or planning to enter it. It gives detailed descriptions of a diversity of modern approaches, and shows how they can be particularly effective and mutually reinforcing when used together. It not only provides a snapshot of current cutting-edge scientific activity in the area, but also offers indications of how the subject is likely to evolve in the future