The Synthesis of Cyclic Combinatorial Circuits

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Exact Stochastic Simulation of Chemical Reactions with Cycle Leaping

The stochastic simulation algorithm (SSA), first proposed by Gillespie, has become the workhorse of computational biology. It tracks integer quantities of the molecular species, executing reactions at random based on propensity calculations. An estimate for the resulting quantities of the different species is obtained by averaging the results of repeated trials. Unfortunately, for models with many reaction channels and many species, the algorithm requires a prohibitive amount of computation time. Many trials must be performed, each forming a lengthy trajectory through the state space. With coupled or reversible reactions, the simulation often loops through the same sequence of states repeatedly, consuming computing time, but making no forward progress. We propose a algorithm that reduces the simulation time through cycle leaping: when cycles are encountered, the exit probabilities are calculated. Then, in a single bound, the simulation leaps directly to one of the exit states. The technique is exact, sampling the state space with the expected probability distribution. It is a component of a general framework that we have developed for stochastic simulation based on probabilistic analysis and caching

Cyclic Boolean circuits

A Boolean circuit is a collection of gates and wires that performs a mapping from Boolean inputs to Boolean outputs. The accepted wisdom is that such circuits must have acyclic (i.e., loop-free or feed-forward) topologies. In fact, the model is often defined this way – as a directed acyclic graph (DAG). And yet simple examples suggest that this is incorrect. We advocate that Boolean circuits should have cyclic topologies (i.e., loops or feedback paths). In other work, we demonstrated the practical implications of this view: digital circuits can be designed with fewer gates if they contain cycles. In this paper, we explore the theoretical underpinnings of the idea. We show that the complexity of implementing Boolean functions can be lower with cyclic topologies than with acyclic topologies. With examples, we show that certain Boolean functions can be implemented by cyclic circuits with as little as one-half the number gates that are required by equivalent acyclic circuits

Algorithmic Aspects of Cyclic Combinational Circuit Synthesis

Digital circuits are called combinational if they are memoryless: if they have outputs that depend only on the current values of the inputs. Combinational circuits are generally thought of as acyclic (i.e., feed-forward) structures. And yet, cyclic circuits can be combinational. Cycles sometimes occur in designs synthesized from high-level descriptions, as well as in bus-based designs [16]. Feedback in such cases is carefully contrived, typically occurring when functional units are connected in a cyclic topology. Although the premise of cycles in combinational circuits has been accepted, and analysis techniques have been proposed [7], no one has attempted the synthesis of circuits with feedback at the logic level. We have argued the case for a paradigm shift in combinational circuit design [10]. We should no longer think of combinational logic as acyclic in theory or in practice, since most combinational circuits are best designed with cycles. We have proposed a general methodology for the synthesis of multilevel networks with cyclic topologies and incorporated it in a general logic synthesis environment. In trials, benchmark circuits were optimized significantly, with improvements of up to 30%I n the area. In this paper, we discuss algorithmic aspects of cyclic circuit design. We formulate a symbolic framework for analysis based on a divide-and-conquer strategy. Unlike previous approaches, our method does not require ternary-valued simulation. Our analysis for combinationality is tightly coupled with the synthesis phase, in which we assemble a combinational network from smaller combinational components. We discuss the underpinnings of the heuristic search methods and present examples as well as synthesis results for benchmark circuits. In this paper, we discuss algorithmic aspects of cyclic circuit design. We formulate a symbolic framework for analysis based on a divide-and-conquer strategy. Unlike previous approaches, our method does not require ternary-valued simulation. Our analysis for combinationality is tightly coupled with the synthesis phase, in which we assemble a combinational network from smaller combinational components. We discuss the underpinnings of the heuristic search methods and present examples as well as synthesis results for benchmark circuits

Reduction of Interpolants for Logic Synthesis

Abstract—Craig Interpolation is a state-of-the-art technique for logic synthesis and verification, based on Boolean Satisfiability (SAT). Leveraging the efficacy of SAT algorithms, Craig Interpolation produces solutions quickly to challenging problems such as synthesizing functional dependencies and performing bounded model-checking. Unfortunately, the quality of the solutions is often poor. When interpolants are used to synthesize functional dependencies, the resulting structure of the functions may be unnecessarily complex. In most applications to date, interpolants have been generated directly from the proofs of unsatisfiability that are provided by SAT solvers. In this work, we propose efficient methods based on incremental SAT solving for modifying resolution proofs in order to obtain more compact interpolants. This, in turn, reduces the cost of the logic that is generated for functional dependencies. I

Trading Weight Size for Circuit Depth: A Circuit for Comparison

NOTE: Text or symbols not renderable in plain ASCII are indicated by [...]. Abstract included in .pdf document. We present an explicit construction of a circuit for the COMPARISON function in [...], the class of polynomial-size linear threshold circuits of depth two with polynomially growing weights. Goldmann and Karpinski proved that [...] in [4]. Hofmeister presented a simplified version of the same result in [6]. We have further simplified the results of these two papers by limiting ourselves to the simulation of COMPARISON. Our construction has size [...], a significant improvement on the general bound of [...] in [6]

Computing in the RAIN: a reliable array of independent nodes

The RAIN project is a research collaboration between Caltech and NASA-JPL on distributed computing and data-storage systems for future spaceborne missions. The goal of the project is to identify and develop key building blocks for reliable distributed systems built with inexpensive off-the-shelf components. The RAIN platform consists of a heterogeneous cluster of computing and/or storage nodes connected via multiple interfaces to networks configured in fault-tolerant topologies. The RAIN software components run in conjunction with operating system services and standard network protocols. Through software-implemented fault tolerance, the system tolerates multiple node, link, and switch failures, with no single point of failure. The RAIN-technology has been transferred to Rainfinity, a start-up company focusing on creating clustered solutions for improving the performance and availability of Internet data centers. In this paper, we describe the following contributions: 1) fault-tolerant interconnect topologies and communication protocols providing consistent error reporting of link failures, 2) fault management techniques based on group membership, and 3) data storage schemes based on computationally efficient error-control codes. We present several proof-of-concept applications: a highly-available video server, a highly-available Web server, and a distributed checkpointing system. Also, we describe a commercial product, Rainwall, built with the RAIN technology

A reconfigurable stochastic architecture for highly reliable computing

Mounting concerns over variability, defects and noise motivate a new approach for integrated circuits: the design of stochastic logic, that is to say, digital circuitry that operates on probabilistic signals, and so can cope with errors and uncertainty. Techniques for prob-abilistic analysis are well established. We advocate a strategy for synthesis. In this paper, we present a reconfigurable architecture that implements the computation of arbitrary continuous functions with stochastic logic. We analyze the sources of error: approxima-tion, quantization, and random fluctuations. We demonstrate the ef-fectiveness of our method on a collection of benchmarks for image processing. Synthesis trials show that our stochastic architecture requires less area than conventional hardware implementations. It achieves a large speed up compared to software conventional im-plementations. Most importantly, it is much more tolerant of soft errors (bit flips) than these deterministic implementations