5,119 research outputs found
Synthesizing SystemC Code from Delay Hybrid CSP
Delay is omnipresent in modern control systems, which can prompt oscillations
and may cause deterioration of control performance, invalidate both stability
and safety properties. This implies that safety or stability certificates
obtained on idealized, delay-free models of systems prone to delayed coupling
may be erratic, and further the incorrectness of the executable code generated
from these models. However, automated methods for system verification and code
generation that ought to address models of system dynamics reflecting delays
have not been paid enough attention yet in the computer science community. In
our previous work, on one hand, we investigated the verification of delay
dynamical and hybrid systems; on the other hand, we also addressed how to
synthesize SystemC code from a verified hybrid system modelled by Hybrid CSP
(HCSP) without delay. In this paper, we give a first attempt to synthesize
SystemC code from a verified delay hybrid system modelled by Delay HCSP
(dHCSP), which is an extension of HCSP by replacing ordinary differential
equations (ODEs) with delay differential equations (DDEs). We implement a tool
to support the automatic translation from dHCSP to SystemC
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical EDA problems suffer from the curse of dimensionality, i.e. the
very fast-scaling computational burden produced by large number of parameters
and/or unknown variables. This phenomenon may be caused by multiple spatial or
temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit
simulation), nonlinearity of devices and circuits, large number of design or
optimization parameters (e.g. full-chip routing/placement and circuit sizing),
or extensive process variations (e.g. variability/reliability analysis and
design for manufacturability). The computational challenges generated by such
high dimensional problems are generally hard to handle efficiently with
traditional EDA core algorithms that are based on matrix and vector
computation. This paper presents "tensor computation" as an alternative general
framework for the development of efficient EDA algorithms and tools. A tensor
is a high-dimensional generalization of a matrix and a vector, and is a natural
choice for both storing and solving efficiently high-dimensional EDA problems.
This paper gives a basic tutorial on tensors, demonstrates some recent examples
of EDA applications (e.g., nonlinear circuit modeling and high-dimensional
uncertainty quantification), and suggests further open EDA problems where the
use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and
System
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