Hybrid partial evaluation (HPE) is a pragmatic approach to partial evaluation that borrows ideas from both online and offline partial evaluation. HPE performs offline-style specialization using an online approach without static binding time analysis. The goal of HPE is to provide a practical and predictable level of optimization for programmers, with an implementation strategy that fits well within existing compilers or interpreters. HPE requires the programmer to specify where partial evaluation should be applied. It provides no termination guarantee and reports errors in situations that violate simple binding time rules, or have incorrect use of side effects in compile-time code. We formalize HPE for a small imperative object-oriented language and describe Civet, a straightforward implementation of HPE as a relatively simple extension of a Java compiler. Code optimized by Civet performs as well as the output of a state-of-the-art offline partial evaluator.

SystemC is a system-level modeling language that can be used effectively for hardware/software co-design. Since a major goal of SystemC is to enable verification at higher levels of abstraction, the tendency is now directing to introducing formal verification approaches for SystemC. In this article, we propose an approach for formal verification of SystemC designs, and provide the semantics of SystemC using Labeled Transition Systems (LTS) for this purpose. An actor-based language, Rebeca, is used as an intermediate language. SystemC designs are mapped to Rebeca models and then Rebeca verification toolset is used to verify LTL and CTL properties. To tackle the state-space explosion, Rebeca model checkers offer some reduction policies that make them appropriate for SystemC verification. The approach also benefits from the modular verification and program slicing techniques applied on Rebeca models. To show the applicability of our approach, we verified a single-cycle MIPS design and two hardware/software co-designs. The results show that our approach can effectively be used both in hardware and hardware/software co-verification.

In this paper, we present a genetically induced oblique decision tree algorithm. In traditional decision tree, each internal node has a testing criterion involving a single attribute. Oblique decision tree allows testing criterion to consist of more than one attribute. Here we use genetic programming to evolve and find an optimal testing criterion in each internal node for the set of samples at that node. This testing criterion is the characteristic function of a relation over existing attributes. We present the algorithm for construction of the oblique decision tree. We also compare the results of our proposed oblique decision tree with the one of C4.5 algorithm.