Preparation of Papers in Two-Column Format for the Proceedings in A4
Architecture for Pseudo Acausal Evolvable 
Embedded Systems 
Mohd Abubakr and Rali Manikya Vinay, 
Electronics and Communication Engineering 
Gokaraju Rangaraju Institute of Engineering and Technology 
Miyapur, Hyderabad, 500045 INDIA 
Email id: mohdabubakr@ieee.org 
Abstract-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.  
I. INTRODUCTION 
Consumer demands are main source of inspiration for 
innovative designs of embedded systems. With the increase 
in demand for the dependable machines, new architectures 
for autonomous devices have emerged in the market. These 
devices include smart phones, smart cars that have the 
capability of taking decision on their own. The success of 
these devices has led the industry to invest towards 
extending autonomous behavior of machine for all 
applications. Research is now seeking a course which makes 
machines much more superior and intelligent that are not 
only 'smart' enough to take decision but can forecast the 
possible robust surroundings it faces and acclimatizes itself 
by modifying itself at the hardware level. Evolvable 
embedded systems are one of such highly demanded and 
invested field in the present research scenario. ‘Evolvable’ 
implies the autonomous behavior of the system to be capable 
of repairing and formulating a solution on its own. This 
autonomous behavior can be achieved through genetic 
programming and artificial intelligence. 
 In this paper, a new class of embedded systems is 
discussed that have certain distinguishable properties.  The 
evolvable concept of embedded systems has been studied 
recently and a generic methodology has been developed [1]. 
We discuss the possibility of using the theoretical framework 
of acausality in developing evolvable embedded systems and 
propose a novel architecture for implementing the same. 
The paper is organized as follows. Motivation and prior 
work discussed in section II. A brief explanation about 
acausality in section III. A proposed architecture to 
implement the projected technique has been furnished in 
section IV. In the sub-sections each block of the architecture 
is briefly discussed. The working of the architecture is 
explained in the section V followed by conclusion.   
_________________________________________ 
Research funded by: GRIET, Miyapur, Hyderabad. 
II. MOTIVATION AND PRIOR WORK 
Conventional embedded systems consist of a micro-
controller and DSP components realized using Field 
programmable gate arrays (FPGA), Complex programmable 
logic arrays (CPLDs), etc. With the increasing trend of 
System on Chip (SoC) integrations, mixed signal design on 
the single chip has become achievable. Such systems are 
excessively used in the areas of wireless communication, 
networking, signal processing, multimedia and networking. 
In order to increase the quality of service  (QoS) the 
embedded system needs to be fault tolerant, must consume 
low power, must have high life time and should be 
economically feasible. These services have become a 
common specification for all the embedded systems and 
consequently to attract attention from commercial market 
different researchers have come up with novel solutions to 
redefine the QoS of embedded systems. Future embedded 
systems consists of evolutionary techniques that repair, 
evolve and adapt themselves to the conditions they are put in. 
Such systems can be termed as autonomous designs. 
Autonomous designs using genetic algorithms and 
artificial intelligence evolve new hardware systems basing 
on the conditions have generated interest in recent days. 
These systems are based upon the adaptive computational 
machines that produce an entirely new solution on its own 
when the environment gets hostile. Here hostile environment 
refers to the changes in temperature, increase in radiation 
content, under these conditions an autonomous systems 
needs to have an ability to modify and evolve hardware that 
is less susceptible to the hostile environment. Classification 
of embedded systems as given in [1] is as  
1) Class 0 (fixed software and hardware): Software as well 
as hardware together are defined at the design time. Neither 
reconfiguration nor adaptation is performed. This class also 
contains the systems with reconfigurable FPGAs that are 
only configured during reset. A coffee machine could be a 
good example. 
2) Class 1 (reconfigurable SW/HW): Software or 
hardware (a configuration of an FPGA) is changed during 
the run in order to improve performance and the utilization 
of resources (e.g. in reconfigurable computing). Evolutionary 
algorithm can be used to schedule the sequence of 
configurations at the compile time, but not at the operational 
time. 
3) Class 2 (evolutionary optimization): Evolutionary 
algorithm is a part of the system. Only some coefficients in 
SW (some constants) or HW (e.g. register values) are 
evolved, i.e. limited adaptability is available. Fitness 
calculation and genetic operations are performed in software. 
Example: an adaptive filter changing coefficients for a fixed 
structure of an FIR filter. 
4) Class 3a  (evolution of programs): Entire programs are 
constructed using genetic programming in order to ensure 
adaptation or high-performance computation. Everything is 
performed in software [2]. 
5) Class 3b (evolution of hardware modules): Entire 
hardware modules are evolved in order to ensure adaptation, 
high-performance computation, fault-tolerance or low-
energy consumption. Fitness calculation and genetic 
operations are carried out in software or using a specialized 
hardware. Reconfigurable hardware is configured using 
evolved configurations. The system typically consists of a 
DSP and a reconfigurable device. Example: NASA JPL 
SABLES [3]. 
6) Class 4 (evolvable SoC): All components of class 3b 
are implemented on a single chip. It means that the SoC 
contains a reconfigurable device. Some of such devices have 
been commercialized up to now, for example, a data 
compression chip [4]. 
7) Class 5 (evolvable IP cores): All components of class 
3b are implemented as IP cores, i.e. at the level of HDL 
source code (Hardware Description Language). It requires 
describing the reconfigurable device at the HDL level as well. 
An approach—called the virtual reconfigurable circuit—has 
been introduced to deal with this problem [15]. Then the 
entire evolvable subsystem can be realized in a single FPGA. 
8) Class 6 (co-evolving components): The embedded 
system contains two or more co-evolving hardware or 
software devices. These co-evolving components could be 
implemented as multiprocessors on a SoC or as evolvable IP 
cores on an FPGA. No examples representing this class are 
available nowadays. 
Figure 1: Evolvable component placed in evolvable embedded 
system.  Components 1–4 represent the environment for the 
evolvable component in this example.  
Any embedded system can be categorized through the 
classes given above. Reconfigurable computing has 
significantly contributed to the idea of have evolvable 
hardware through dynamically upload/remove the hardware 
components from the hardware module library.  According 
to [1], an evolvable embedded system can be defined as “a 
reconfigurable embedded system in which an evolutionary 
algorithm is utilized to dynamically modify some of system 
(software and/or hardware) components in order to adapt the 
behavior of the system to a changing environment”. Figure 1 
shows a general block diagram for evolvable embedded 
system.  
III. ACAUSALITY 
Embedded systems design is based on the assumption that 
it generates an output based on present and past inputs. 
These set of embedded systems are called as real time 
embedded systems. The new set of embedded systems that is 
introduced here is based on the assumption that the output is 
generated considering even the future inputs. Such systems 
can be termed as Acausal Embedded Systems. Due to the 
uncertainty in prediction of the future inputs these systems 
require a specialized master algorithm that evolves hardware 
to implement specified output, and this evolved hardware is 
constructed via available resources.  
We define acausality as the term denoted to the systems 
whose present outputs are dependent on past, present and 
future inputs. Acausality is in total contrast with the present 
convention of embedded systems that purely rely on present 
and past inputs.  
IV. PROPOSED SYSTEM 
Various proposals for autonomous embedded systems are 
available in the literature [16]. The system proposed here 
belongs to Class 6 group of systems. It can evolve hardware 
and software on its own using artificial intelligence 
algorithms and available set of resources. Another major 
block of the proposed architecture is the use of future input 
predictor. The details about the future input predictors are 
explained in the next sections. Use of artificial intelligence in 
determining suitable solution for the predicted future inputs 
is essential and plays an important role in determining the 
efficiency of the system. 
The figure 2 shows the proposed architecture of an acausal 
system belonging to the class6 group of evolvable embedded 
systems. It can evolve hardware and software on its own 
using artificial intelligence algorithms and available set of 
resources. The contemporary technology allows the 
embedded hardware creator to make use of the 
reconfigurable hardware resources to build in an evolved 
design. Some reliable reconfigurable hardware platforms can 
be Programmable Analog Array, Field Programmable Gate 
Array (FPGA), FPAA (Field Programmable Analog Array), 
FPTA (Field Programmable Transistor Array), nano devices, 
reconfigurable antennas, MEMS (micro electromechanical 
systems), reconfigurable optics and some other few selective 
components. 
The next sections give the generic description about each 
block present in the proposed architecture shown in the 
figure 2.  
A. Past Input Summarizer 
Functionality of the past input summarizer (PIS) is to store 
the past values of inputs into allocated memory. Since it is 
practically impossible to store all the past inputs in a memory,  
Figure 2: The generalized architecture of the proposed model  
hence we use an algorithm to compress and summarize the 
past inputs. Hence we can define PIS as “it stores relevant 
information of the past inputs which can be applicable in 
predicting the future inputs, in a limited memory by 
following an optimized algorithm”.  The compression and 
summarization algorithms can be chosen based upon the 
sensitivity of the application. Conventional embedded 
systems have very limited memory due to the space 
constraints. Utilization of newly developed optical memory 
concept can be beneficial in increasing the potential of past 
inputs storage [17]. 
B. Present Input  
Acquisition of present inputs is the fundamental 
functionality of this block. There may be wide varieties of 
inputs depending on the applications i.e. the inputs such as 
signals obtained through sensor elements, transmission 
receiver, transducer, etc. At this juncture noise factors are 
taken into consideration; consequently this block plays an 
important role in determining the efficiency of the intact 
embedded system. After execution of a particular input it is 
sent to past input summarizer and a new input is extracted. It 
is a requisite that noise gets eliminated at this block itself 
else the noise subsumes at past input summarizer.  
C. Future Input Predictor and Analyzer  
Future input predictor (FIP) block as the name indicates 
predicts the future inputs. Here an allotment is made to pass 
on future inputs through another device as well. This 
allotment is done using either antenna or some special sensor 
networks. Sometimes, future inputs can be known through 
external agents, hence this allotment forms an interface 
between the external agent and the system. This provision 
makes this block exhibit dual stability in predicting the 
future inputs. Many algorithms for future prediction are 
available in literature. Future prediction and estimates are 
extensively used in financial decisions. Future inputs can 
also be predicted using the data obtained from past inputs.  
Using pattern recognition algorithms for discrete sets of past 
inputs, the future inputs can be determined. There are plenty 
of algorithms that are develops to solve pattern recognition 
from the available data [5,6,7,8].   
D. Embedded Hardware Creator 
This is the heart of acausal embedded system, as it is the 
most important block of all. Embedded Hardware creator 
(EHC) should have an autonomous capability i.e. should be 
capable of making decisions. This can be achieved using 
advance techniques such as artificial intelligence (adaptable 
neural networks), genetic computing, evolutionary 
computing etc.  
Artificial Neural networks (ANN) are models of human 
brain used to perform tasks based on self-learning and 
adaptation to environment. Creating ANNs that can learn and 
generalize the information from surrounding is the first step 
in having autonomous computational machines. Due to 
practical inabilities the learning time for ANNs is very high 
[9,10]. Another mechanism that can help in realizing EHC is 
cellular automata. Cellular automata are discrete spatially 
extended dynamical systems that have been extensively 
studied as a model of computational devices [11,12]. 
Evolutionary algorithms such as genetic algorithms have 
generated a huge interest in this area. Genetic algorithms are 
based upon selection and mutation [13,14]. Such techniques 
can be useful in implementing EHC.  
E. Available Hardware Resources 
For the construction of the hardware devices certain 
amount of resources are allocated to the embedded system 
creator. These allocated resources are based upon the future 
need for up-gradation and cost of the total embedded system. 
FPGA, FPAA, PALS, memory elements, etc are few such 
examples of reconfigurable devices that can be used as 
resources for the embedded hardware creator. A provision is 
provided to increase or repair the available resources in order 
to upgrade the system at any moment. Size of the available 
resources is a constraint since it induces the tendency of the 
system to be more susceptible to thermal noise effects and 
radiation. In order to reduce the unwanted noise effects 
caused by thermal variance and incoming radiation proper 
shielding techniques are needed else available resources may 
become defective. Another parameter that determines the 
number of available resources is the cost. The economic 
feasibility of the embedded system is crucial for the 
commercial success.  
F. Evolved Architecture constructor  
This block is the final evolved design done by the 
embedded hardware creator. This block has inbuilt design 
features to execute the instructions given by EHC into 
building up an actual hardware design. It also consists of 
read/write/erase mechanisms for FPGA, FPAA, FPTA and 
other memories. Hence it forms an interface between EHC 
and Available resources. 
V. WORKING OF THE SYSTEM  
The sequence of operations that go in the system are as 
follows. First the past input summarizer (PIS), the present 
input and the future input predictor (FIP) give realizable 
inputs to the embedded hardware creator and then by 
following a relevant logic a layout for the new derived 
design results as an outcome from the embedded hardware 
creator depending upon the inputs from PIS, present input 
and FIP. The EHC is accountable to sending instructions to 
EAC about the construction of the system using available 
hardware resources. After receiving the instructions from 
EHC, EAC launches the concrete effort of writing and 
erasing of the reconfigurable hardware resources, connecting 
interconnects, etc to construct the working structure of the 
predicted solution. The potential capacity of the hardware 
depends on the type of system that is used. The construction 
of hardware design is not a single step process but a 
continuous process that repeats itself for a more resourceful 
and well-organized design. The proposed system is a 
generalized version that can be modified as per the 
application in which the concept of acausal self-evolving 
reconfigurable hardware is used. 
VI. CONCLUSION 
In this paper we have proposed a model that uses the 
theoretical framework of acausality. The proposed 
architecture is a generalized version of evolvable 
architectures and basing on the application it can be suitably 
modified. The proposal of pseudo acausal evolvable 
embedded systems opens up a path for a new era of research 
and the pace of technological changes assume a new shape 
where we find the machines repairing themselves and 
evolving autonomously removing the major bottle-necks of 
maintenance and non-durable nature of the existing 
embedded systems. This implementation of such technology 
finds itself an imperative place in every field of application. 
Some if its prospective aspects viewed in near future are in 
aeronautics, astronautics, robotics, etc. This technology may 
develop as a capstone for evolvable embedded system 
applications and AI research.  
The generalized concept of modeling evolvable embedded 
systems have been realized in terms of reconfigurable 
components and artificial intelligence, our future research 
will be in creating tools for such design. Due to financial 
constraints we have restricted our work only up to theoretical 
work and we hope in near future to practically demonstrate 
such a system. 
REFERENCES 
[1]  Lukas Sekanina and Vladimır Drabek, Theory and Applications of 
Evolvable Embedded Systems, Proc. Of 11th International Conference 
on on Engineering of Computer based systems, May 2004 
[2] M. Love, K. R. Sorensen, J. Larsen, and J. Clausen. 
DisruptionManagement for an Airline – Rescheduling of Aircraft. In 
Applications of Evolutionary Computing, EvoWorkshops 2002, volume 
2279 of LNCS, pages 315–324. Springer- Verlag, 2002. 
[3] A. Stoica, R. S. Zebulum, D. Keymeulen, M. I. Ferguson, and X. Guo. 
Evolving Circuits in Seconds: Experiments with a Stand-Alone Board 
Level Evolvable System. In Proc.of the 2002 NASA/DoD Conference 
on Evolvable Hardware, pages 67–74, Alexandria, Virginia, 2002. 
IEEE Computer Society. 
[4] M. Tanaka, H. Sakanashi, M. Salami, M. Iwata, T. Kurita, and T. 
Higuchi. Data Compression for Digital Color  Electrophotographic 
Printer with Evolvable Hardware. In Proc.of the 2nd Int. Conf. on 
Evolvable Systems: From Biology to Hardware ICES’98, volume 1478 
of LNCS, pages 106–114, Lausanne, Switzerland, 1998. Springer-
Verlag. 
[5]  L. Devroye,. Gy¨orfi, G. Lugosi, A probabilistic theory of pattern 
recognition. New York: Springer, 1996. 
[6]  M. Kearns M. and U. Vazirani An Introduction to Computational 
Learning Theory. The MIT Press, Cambridge, Massachusetts, 1994. 
[7]  V. Vapnik, Statistical Learning Theory, New York etc.: John Wiley & 
         Sons, Inc. 1998 
[8]  Daniil Ryabko, Pattern Recognition for Conditionally          
Independent Data, CS.LG/0507040 
[9]  X.Yao, Evolutionary Artificial Neural Networks, Int. J. Neural         
Systems, Vol 4. pp 203-222, 1993. 
[10]  B.Muller and J.Reinhardt, Neural Networks, An Introduction, 
springer-verlag 1990.  
[11] H.A. Gutowitz, editor , Cellular Automata, MIT press, Cambridge, MA, 
1990 
[12] T.Toffoli, N.Margolus, Cellular Automata Machines, A new  
environment for modeling, MIT press, Cambridge, MA, 1987 
[13]  C.R. Stephens, I. Garc´ıa Olmedo, J. Mora Vargas, H. Waelbroeck, 
Self Adaptation in evolving systems adap-org/9708002 
[14] T. B¨ack, Evolutionary Algorithms in Theory and Practice: evolution 
strategies, evolutionary programming, genetic algorithms, (Oxford 
Univ. Press 1996). 
[15]  L. Sekanina. Towards Evolvable IP Cores for FPGAs.In Proc. of the 
2003 NASA/DoD Conference on EvolvableHardware, pages 145–154, 
Chicago, IL, 2003. IEEE ComputerSociety. 
[16] L. Sekanina. Evolvable Components: From Theory to Hardware 
Implementations. Natural Computing Series, Springer Verlag, 2004. 
[17] Mohd Abubakr, R.M.Vinay, Novel Technique for Volatile Optical 
Memory using solitons, Proceeding of IEEE WOCN Bangalore, 2006

  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.

Introduction to Computational 
Learning Theory. The MIT Press, Cambridge, Massachusetts, 1994. 
[7]  V. Vapnik, Statistical Learning Theory, New York etc.: John Wiley & 
         Sons, Inc. 1998 
[8]  Daniil Ryabko, Pattern Recognition for Conditionally          
Independent Data, CS.LG/0507040 
[9]  X.Yao, Evolutionary Artificial Neural Networks, Int. J. Neural         
Systems, Vol 4. pp 203-222, 1993. 
[10]  B.Muller and J.Reinhardt, Neural Networks, An Introduction, 
springer-verlag 1990.  
[11] H.A. Gutowitz, editor , Cellular Automata, MIT press, Cambridge, MA, 
1990 
[12] T.Toffoli, N.Margolus, Cellular Automata Machines, A new  
environment for modeling, MIT press, Cambridge, MA, 1987 
[13]  C.R. Stephens, I. Garc´ıa Olmedo, J. Mora Vargas, H. Waelbroeck, 
Self Adaptation in evolving systems adap-org/9708002 
[14] T. B¨ack, Evolutionary Algorithms in Theory and Practice: evolution 
strategies, evolutionary programming, genetic algorithms, (Oxford 
Univ. Press 1996). 
[15]  L. Sekanina. Towards Evolvable IP Cores for FPGAs.In Proc. of the 
2003 NASA/DoD Conference on EvolvableHardware, pages 145–154, 
Chicago, IL, 2003. IEEE ComputerSociety. 
[16] L. Sekanina. Evolvable Components: From Theory to Hardware 
Implementations. Natural Computing Series, Springer Verlag, 2004. 
[17] Mohd Abubakr, R.M.Vinay, Novel Technique for Volatile Optical 
Memory using solitons, Proceeding of IEEE WOCN Bangalore, 2006
