Yinnon A. Haviv

Self-stabilizing microprocessor: analyzing and overcoming soft errors

Soft errors are changes in memory value caused by external radiation or electrical noise. Decreases in computing feature sizes and power usages and shorting the microcycle period enhance the influence of soft errors. Self-stabilizing systems are designed to be started in an arbitrary, possibly a corrupted, state due to, say, soft errors, and to converge to a desired behavior. Self-stabilization is defined by the state space of the components and is essentially a well-founded, clearly defined form of the terms self-healing, automatic-recovery, automatic-repair, and autonomic-computing. To implement a self-stabilizing system, one needs to ensure that the microprocessor that executes the program is self-stabilizing. A self-stabilizing microprocessor copes with any combination of soft errors, converging to perform fetch-decode-execute in fault-free periods. Still, it is important that the microprocessor will avoid convergence periods if possible by masking the effect of soft errors immediately. In this work, we present design schemes for a self-stabilizing microprocessor and a new technique for analyzing the effect of soft errors. Previous schemes for analyzing the effect of soft errors were based on simulations. In contrast, our scheme computes a lower bound on microprocessor reliability and enables the microprocessor designer to evaluate the reliability of the design and to identify reliability bottlenecks. When analyzing the resiliency of digital circuits to soft errors, we examine the logical masking, i.e., errors in internal nodes of the circuits that are masked later by the computation. We show that the problem of computing the reliability of a circuit such that logical masking is taken into account is an NP-hard problem.

Self-Stabilization as a Foundation for Autonomic Computing

This position paper advocates the use of the well defined and provable self-stabilization property of a system, to achieve the goals of the self-* paradigms and autonomic computing. Several recent results starting from hardware concerns, continuing with the operating system, and ending in the applications, are integrated: the self-stabilizing microprocessor, with the self-stabilizing operating system, the self-stabilization preserving compiler, and the self-stabilizing autonomic recoverer for applications

Self-stabilization preserving compiler

Self-Stabilization is an elegant approach for designing fault tolerant systems. A system is considered self-stabilizing if, starting in any state, it converges to the desired behavior. Self-stabilizing algorithms were designed for solving fundamental distributed tasks, such as leader election, token circulation and communication network protocols. The algorithms were expressed using guarded commands or pseudo-code. The realization of these algorithms requires the existence of (self-stabilizing) infrastructure for their execution such as a self-stabilizing microprocessor and a self-stabilizing operating system. Moreover, the high-level description of the algorithms needs to be converted into machine language of the microprocessor. In this work, we present a design for a self-stabilization preserving compiler designed for programs written in a language similar to the abstract state machine (ASM). The compiler preserves the stabilization property of the high level program.

Self-Stabilizing Microprocessor

Soft-errors are changes in memory value caused by cos- mic rays. Decrease in computing features size, decrease in power us- age and shorting the micro-cycle period, enhances the influence of soft- errors. Self-stabilizing systems is designed to be started in an arbi- trary, possibly corrupted state, due to, say, soft errors, and to converge to a desired behavior. Self-stabilization is defined by the state space of the components, and essentially is a well founded, clearly defined, form of the terms: self-healing, automatic-recovery, automatic-repair, and autonomic-computing. To implement a self-stabilizing system one needs to ensure that the micro-processor that executes the program is self-stabilizing. The self-stabilizing microprocessor copes with any combi- nation of soft errors, converging to perform fetch-decode-execute in fault free periods. Still, it is important that the micro-processor will avoid convergence periods as possible, by masking the effect of soft errors im- mediately. In this work we present design schemes for self-stabilizing microprocessor, and a new technique for analyzing the effect of soft er- rors. Previous schemes for analyzing the effect of soft errors were based on simulations. In contrast, our scheme computes lower bound on the micro-processor reliability and enables the micro-processor designer to evaluate the reliability of the design, and to identify reliability bottle- necks.

Brief Announcement: Unique Permutation Hashing

We propose a new open addressing hash function, the unique-permutation hash function , and a performance analysis of its hash computation. A hash function h is simple uniform if items are equally likely to be hashed to any table location (in the first trial). A hash function h is random or strong uniform if the probability of any permutation to be a probe sequence, when using h , is

Stabilization enabling technology

{{1}\over{N!}}

Stabilization Enabling Technology

, where N is the size of the table. We show that the unique-permutation hash function is strong uniform and therefore has the lowest expected cost; each probe sequence is equally likely to be chosen, when the keys are uniformly chosen. Thus, the unique-permutation hash ensures that each empty table location has the same probability to be assigned with a uniformly chosen key. For constant load factors *** < 1, where *** is the ratio between the number of inserted items and the table size, the expected time for computing the unique-permutation hash function is O (1) and the expected number of table locations that are checked before an empty location is found, during insertion (or search), is also O (1).