4,155 research outputs found
Formal Verification of an Iterative Low-Power x86 Floating-Point Multiplier with Redundant Feedback
We present the formal verification of a low-power x86 floating-point
multiplier. The multiplier operates iteratively and feeds back intermediate
results in redundant representation. It supports x87 and SSE instructions in
various precisions and can block the issuing of new instructions. The design
has been optimized for low-power operation and has not been constrained by the
formal verification effort. Additional improvements for the implementation were
identified through formal verification. The formal verification of the design
also incorporates the implementation of clock-gating and control logic. The
core of the verification effort was based on ACL2 theorem proving.
Additionally, model checking has been used to verify some properties of the
floating-point scheduler that are relevant for the correct operation of the
unit.Comment: In Proceedings ACL2 2011, arXiv:1110.447
Decision-making and problem-solving methods in automation technology
The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming
Premise Selection and External Provers for HOL4
Learning-assisted automated reasoning has recently gained popularity among
the users of Isabelle/HOL, HOL Light, and Mizar. In this paper, we present an
add-on to the HOL4 proof assistant and an adaptation of the HOLyHammer system
that provides machine learning-based premise selection and automated reasoning
also for HOL4. We efficiently record the HOL4 dependencies and extract features
from the theorem statements, which form a basis for premise selection.
HOLyHammer transforms the HOL4 statements in the various TPTP-ATP proof
formats, which are then processed by the ATPs. We discuss the different
evaluation settings: ATPs, accessible lemmas, and premise numbers. We measure
the performance of HOLyHammer on the HOL4 standard library. The results are
combined accordingly and compared with the HOL Light experiments, showing a
comparably high quality of predictions. The system directly benefits HOL4 users
by automatically finding proofs dependencies that can be reconstructed by
Metis
Verification of the FtCayuga fault-tolerant microprocessor system. Volume 1: A case study in theorem prover-based verification
The design and formal verification of a hardware system for a task that is an important component of a fault tolerant computer architecture for flight control systems is presented. The hardware system implements an algorithm for obtaining interactive consistancy (byzantine agreement) among four microprocessors as a special instruction on the processors. The property verified insures that an execution of the special instruction by the processors correctly accomplishes interactive consistency, provided certain preconditions hold. An assumption is made that the processors execute synchronously. For verification, the authors used a computer aided design hardware design verification tool, Spectool, and the theorem prover, Clio. A major contribution of the work is the demonstration of a significant fault tolerant hardware design that is mechanically verified by a theorem prover
Fast-Convergent Learning-aided Control in Energy Harvesting Networks
In this paper, we present a novel learning-aided energy management scheme
() for multihop energy harvesting networks. Different from prior
works on this problem, our algorithm explicitly incorporates information
learning into system control via a step called \emph{perturbed dual learning}.
does not require any statistical information of the system
dynamics for implementation, and efficiently resolves the challenging energy
outage problem. We show that achieves the near-optimal
utility-delay tradeoff with an
energy buffers (). More interestingly,
possesses a \emph{convergence time} of , which is much faster than the time of
pure queue-based techniques or the time of approaches
that rely purely on learning the system statistics. This fast convergence
property makes more adaptive and efficient in resource
allocation in dynamic environments. The design and analysis of
demonstrate how system control algorithms can be augmented by learning and what
the benefits are. The methodology and algorithm can also be applied to similar
problems, e.g., processing networks, where nodes require nonzero amount of
contents to support their actions
Learning-aided Stochastic Network Optimization with Imperfect State Prediction
We investigate the problem of stochastic network optimization in the presence
of imperfect state prediction and non-stationarity. Based on a novel
distribution-accuracy curve prediction model, we develop the predictive
learning-aided control (PLC) algorithm, which jointly utilizes historic and
predicted network state information for decision making. PLC is an online
algorithm that requires zero a-prior system statistical information, and
consists of three key components, namely sequential distribution estimation and
change detection, dual learning, and online queue-based control.
Specifically, we show that PLC simultaneously achieves good long-term
performance, short-term queue size reduction, accurate change detection, and
fast algorithm convergence. In particular, for stationary networks, PLC
achieves a near-optimal , utility-delay
tradeoff. For non-stationary networks, \plc{} obtains an
utility-backlog tradeoff for distributions that last
time, where
is the prediction accuracy and is a constant (the
Backpressue algorithm \cite{neelynowbook} requires an length
for the same utility performance with a larger backlog). Moreover, PLC detects
distribution change slots faster with high probability ( is the
prediction size) and achieves an convergence time. Our results demonstrate
that state prediction (even imperfect) can help (i) achieve faster detection
and convergence, and (ii) obtain better utility-delay tradeoffs
Learning-Assisted Automated Reasoning with Flyspeck
The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this architecture is evaluated in a bootstrapping scenario
emulating the development of Flyspeck from axioms to the last theorem, each
time using only the previous theorems and proofs. It is shown that 39% of the
14185 theorems could be proved in a push-button mode (without any high-level
advice and user interaction) in 30 seconds of real time on a fourteen-CPU
workstation. The necessary work involves: (i) an implementation of sound
translations of the HOL Light logic to ATP formalisms: untyped first-order,
polymorphic typed first-order, and typed higher-order, (ii) export of the
dependency information from HOL Light and ATP proofs for the machine learners,
and (iii) choice of suitable representations and methods for learning from
previous proofs, and their integration as advisors with HOL Light. This work is
described and discussed here, and an initial analysis of the body of proofs
that were found fully automatically is provided
Throughput Scaling of Wireless Networks With Random Connections
This work studies the throughput scaling laws of ad hoc wireless networks in
the limit of a large number of nodes. A random connections model is assumed in
which the channel connections between the nodes are drawn independently from a
common distribution. Transmitting nodes are subject to an on-off strategy, and
receiving nodes employ conventional single-user decoding. The following results
are proven:
1) For a class of connection models with finite mean and variance, the
throughput scaling is upper-bounded by for single-hop schemes, and
for two-hop (and multihop) schemes.
2) The throughput scaling is achievable for a specific
connection model by a two-hop opportunistic relaying scheme, which employs
full, but only local channel state information (CSI) at the receivers, and
partial CSI at the transmitters.
3) By relaxing the constraints of finite mean and variance of the connection
model, linear throughput scaling is achievable with Pareto-type
fading models.Comment: 13 pages, 4 figures, To appear in IEEE Transactions on Information
Theor
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