摘要翻译：
半导体技术的进步使得嵌入式系统的设计越来越复杂。具有诸如可演化性和自治行为等新技术的体系结构已经引起了人们的广泛关注。本文从概念上论证了嵌入式系统的可演化性可以根据无尾性来刻画。注意到在无尾系统中，未来的输入是需要知道的，这里我们提出了一个机制，使系统预测未来的输入，并表现出伪无尾性质。提出了一种基于无源性理论框架的嵌入式系统。我们的方法旨在建立一种新的体系结构，该体系结构具有硬件的可进化性和自治行为，同时具有伪随机性。详细讨论了该体系结构的各个方面以及局限性。
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英文标题：
《Architecture for Pseudo Acausal Evolvable Embedded Systems》
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作者：
Mohd Abubakr, R.M.Vinay
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Advances in semiconductor technology are contributing to the increasing complexity in the design of embedded systems. Architectures with novel techniques such as evolvable nature and autonomous behavior have engrossed lot of attention. This paper demonstrates conceptually evolvable embedded systems can be characterized basing on acausal nature. It is noted that in acausal systems, future input needs to be known, here we make a mechanism such that the system predicts the future inputs and exhibits pseudo acausal nature. An embedded system that uses theoretical framework of acausality is proposed. Our method aims at a novel architecture that features the hardware evolability and autonomous behavior alongside pseudo acausality. Various aspects of this architecture are discussed in detail along with the limitations.
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