High-Performance Energy-Efficient and Reliable Design of Spin-Transfer Torque Magnetic Memory

In this dissertation new computing paradigms, architectures and design philosophy are proposed and evaluated for adopting the STT-MRAM technology as highly reliable, energy efficient and fast memory. For this purpose, a novel cross-layer framework from the cell-level all the way up to the system- and application-level has been developed. In these framework, the reliability issues are modeled accurately with appropriate fault models at different abstraction levels in order to analyze the overall failure rates of the entire memory and its Mean Time To Failure (MTTF) along with considering the temperature and process variation effects. Design-time, compile-time and run-time solutions have been provided to address the challenges associated with STT-MRAM. The effectiveness of the proposed solutions is demonstrated in extensive experiments that show significant improvements in comparison to state-of-the-art solutions, i.e. lower-power, higher-performance and more reliable STT-MRAM design

A Computational Framework for Efficient Error Correcting Codes Using an Artificial Neural Network Paradigm.

The quest for an efficient computational approach to neural connectivity problems has undergone a significant evolution in the last few years. The current best systems are far from equaling human performance, especially when a program of instructions is executed sequentially as in a von Neuman computer. On the other hand, neural net models are potential candidates for parallel processing since they explore many competing hypotheses simultaneously using massively parallel nets composed of many computational elements connected by links with variable weights. Thus, the application of modeling of a neural network must be complemented by deep insight into how to embed algorithms for an error correcting paradigm in order to gain the advantage of parallel computation. In this dissertation, we construct a neural network for single error detection and correction in linear codes. Then we present an error-detecting paradigm in the framework of neural networks. We consider the problem of error detection of systematic unidirectional codes which is assumed to have double or triple errors. The generalization of network construction for the error-detecting codes is discussed with a heuristic algorithm. We also describe models of the code construction, detection and correction of t-EC/d-ED/AUED (t-Error Correcting/d-Error Detecting/All Unidirectional Error Detecting) codes which are more general codes in the error correcting paradigm

Concurrent error detection

Concurrent error detection (CED) is the detection of errors or faults in a circuit or data path concurrent with normal operation of that circuit. The general approach for CED is to calculate a check symbol for the inputs to the circuit under operation, predict the check symbol that will result for the output of the circuit for those inputs, and compare the predicted check symbol to the one that is actually calculated for the output. If the predicted and actual check symbols are different, an error or fault has been detected. The alternative to this check symbol prediction is to use a second copy of the circuit under operation and compare the results of the two circuits. For some classes of circuits the prediction of the output check symbol can require less circuitry than a second copy of the circuit being tested. Four examples of these types of circuits are examined in this dissertation: Arithmetic Logic Units (ALUs), array multipliers, self-synchronous scrambler-descrambler pairs with their intervening data path, and switch fabrics. Faults in integrated circuits tend to produce unidirectional errors. Unidirectional errors are those in which all of the errors are in the same direction (e.g., 0 to 1 errors) within the block of data covered by a given check symbol. For this reason, codes that are optimized for unidirectional errors are the focus of investigation for most of the applications. In particular, the Bose-Lin codes are examined for those applications where unidirectional errors are expected to be typical. In order to examine the performance of the Bose-Lin codes in one of these applications, it was necessary to determine the theoretical performance for Bose- Lin codes for error rates beyond what had been previously studied. This analysis of Bose-Lin codes with large numbers of "burst" errors also included a further generalization of the codes

A t-unidirectional error-detecting systematic code

AbstractA systematic code consists of codewords in which the check symbol is appended to the information symbol. Thus, data manipulation and encoding/decoding can be done in parallel. The Berger code is a well-known optimal systematic code for detecting all unidirectional errors. In VLSI circuits most of the errors are found to be unidirectional in nature. However, in many applications it may not be necessary to detect all unidirectional errors. Most faults, unless catastrophic in nature, do not cause errors in all the bits of the information and check symbol. Therefore, it may be enough to guarantee detection of every unidirectional error in t or fewer bits of the codeword, if t is reasonably large. In this paper we present such a t-unidirectional error-detecting code

Diversity combining ARQ over the m(> 2)-ary unidirectional channel

In diversity combining automatic repeat request (ARQ), erroneous packets are combined together forming a single, more reliable, packet. In this thesis, we give a diversity combining scheme for the m-ary unidirectional channel. A system using the given scheme with a t-unidirectional error detecting code is able to correct up to Emax = floor(t/2) unidirectional errors. To use the given scheme, the decoder should be able to decide the error type (increasing or decreasing). Hence, we give simple techniques to make this decision for various unidirectional error detecting codes