From Darwin to Darware: Why computer engineers havestarted listening to Mother (Nature).

MOSHE SIPPER, DANIEL MANGE, AND EDUARDO SANCHEZ

rent engineering capabilities. Nature is the grandest engineer known to man-though

a blind one at that: her designs come into existence through the slow process, over mil-

lions of years, known as evolution by natUral selection [2]. As put forward by Charles

Darwin in his 1859 masterpiece, On the Origin of Species, evolution is based on "...one

general law, leading to the advancement of all organic beings, namely, multiply, vary, let

the strongest live and the weakest die." [1].

50 ApriI999/Vd,42.No4 COMMUNICAnONSOfnllACM

.,\1)0u I

~.

hemer one looks outside the window or stands in

&ont of a mirror, nature's work is evident in all its

glory. Be it a pine tree, a flea, or a human being,

nature has designed highly complex machines, the

construction of which is still well beyond our cur-Draf

t

The evolutionary p~ is based on four basicprinciples:

. Individual organisms vary in viability in the envi-ronments that they occupy.. This variation is heritable.

. Individuals tend to produce mole offspring thancan survive on the limited resources available in theenvironment.. In the ensuing struggle for survival, theindividuals best adapted to the environment arethe ones mat will survive to reproduce.

The continual workings of this process overthe millennia cause populations of organisms to~ge. generally becoming better adapted to theirenVIronments.

Evolution has not only produced ingenious solu-tions to specific problems--for example. structUraldesigns such as eyes or wing.t-but indeed has found(and founded) entirely new processes to aid in theemergence of complex organisms. Two of the mostimportant ones are ontogmy and /earning.

Most natural organisms consist of nwnerous ele-mental units called cells (for example. a humanbeing is composed of

approximately sixtytrillion cells). Eachand every cell con-tains the entire plan of theorganism. known as the gmoml': aone-dimensional chain of deoxyribonu-cleic acid (DNA) that contains theinstructions necessary for the making of theindividual. Known also as multicellular organisms.they develop through the process of ontogeny.which involves the successive divisions of a fertilizedmother cell, ultirnatdy resulting in a completebeing. The genome--or gmolypt'--mus gives rise tothe so-called phmotypl': the mature organism thatemerges from the ontogenetic process. The geno-type-phenotype distinction is fundamental innature: while it is the phenotype that is subjected tothe survival battle. it is the genotype that retains theevolutionary benefits.

Ontogeny can be viewed as one of nature'stricks for combating the information explosioninherent in. the definition of a complex design:rather than de6ne a one-to-one plan (a homuncu-lus). nature uses a compressed definition that ismore akin to a recipe or a construction program.Further compression can be attained by introduc-ing the process of learning. The organism that

emerges from the ontogenetic process is not fullyequipped to handle every aspect of its daily exis-tence, but rather proceeds to improve its behaviorby learning how to cope with new sitUations asr;bey are encountered (this is most notable amongmammals). The introduction of learning reducesthe amount of information that needs to beencoded in the genome.

Over the past few years a growing number ofcomputing scientists and engineers have been turn-ing to natUre, seeking inspiration to augment thecapabilities of their artificial systems. In a recentpaper [10], Sipper et al. noted that such bio-inspirrJsystems can be partitioned along three axes, corre-

sponding to the three processes mentionedpreviously: phylogeny (evolution),ontogeny, and epigenesis (learning); theydubbed this the POE model. Whileresearch in this area can be traced back todte 1950s, when the first computerswere used to carry out simulationsbased on these ideas, much of the

recent work is centered on the con-struction of actual hardwaredevices.

The ultimate goal ofbio-inspired system engineers isto create more adaptive systems,in which .adaptive" refers to a

system's ability to undergo modifi-cations according to changing circum-

stances, thus ensuring its continuedfunctionality. One often speaks of an envi-

ronment and of a system's adjustment to changingenvironmental conditions. The fact that hardwareevolves is perhaps not surprising in and of itself,after all, the Intel 80x86 .species" evolved from amere 29,000 transistors in 1978 (the 8086) to7 ,500,000 transistors in 1997 (the Pentium II), notto mention the many funaionalities that have beenadded over the years. I Thus, the processor has

adapted to its environment, which is, ultimately, theusers. This evolutionary process involves what issometimes referred to as the "hand of God": theengineer who designs the chip. WIth bio-inspiredhardware the aim is to reduce the amount of humandesign necessary. The ultimate goal is to build a sys-tem that will evolve, develop (in an ontogeneticsense). and learn-in short, adapt~n its own, thatis, with no human intervention. As an example,

ofIS

C_1CA11ONS Of - Aat ApriI999/VaL 42. No. 4 5 I

imagine some future chip family that evolves on itsown from the "8086" epoch to the "Pentium"epoch. Obviously, it will need to be equipped withthe ability to accrue additional material resources (aswith natural evolution); what is far from obvious atthis point is how to induce this kind of ability. Insummary, we want to replace the human engineerwith a blind yet potentially more powerful one.

There is still a long road to travel before we cancreate an autonomously evolving system. Our intentin this article is to describe a number of currentmilestones, and to trace some of the possible devel-opments of the near future.

From Carbon to Silicon:Configurable CircuitsWithin the field of bio-inspired hardware, a majorenabling technology is that of configurable circuits,and especially field-programmable gate arrays, orFPGAs. FPGAs, which have been coming of ageover the past few years, are large, fast integrated cir-cuits that can be modified or configured at almostany point by the end user [12].

The primary distinction that this technologybrings about is that between programmabk circuitsand configurabk ones [7]. A programmable circuitceasdessly iterates through a three-phase loop, wherean instruction is first fttched from memory, afterwhich it is decoded, then to be passed on to the finalexecute phase. The execute phase may require severalclock cycles; the process is then repeated for the nextinstruction, and so on. A configurable circuit, on theother hand, can be regarded as having a single, non-iterative fetch phase: the so-called configurationstring, fetched from mem-ory, requires no furtherinterpretation, and isdirectly used to configurethe hardware. No furtherphases or iterations areneeded, as the circuit is nowconfigured for the task athand. The ability to controlthe hardware in such a directmanner is a double-edgedsword: the user is able to

der rangewith the

price to be paid being amore arduous design task.

Within the domain ofconfigurable computingone can distinguish betweentwo types of configuration

5:1 Aprt ""/Vd.42. No. 4 COMMUNlCA110NSOPntEACM

strings: static and dynamic [7]. A static configura-tion string, aimed at configuring the circuit to per-form a given function, is loaded once at the outset,after which it does not change during the executionof "the task at hand. Static applications are mainlyaimed at attaining the classic goal in computing:that of improving performance, either in terms ofspeed, resource utilization, or area usage. Dynamicconfigurability involves a configuration string thatcan change during execution of the task at hand.Dynamic systems are able to undergo modificationsaccording to changing circumstances, thus continu-ing to function within their dynamic environments.As such, they represent an excellent substrate forimplementing the adaptive systems discussed in thisarticle.

Figure I. The Firefly evolware board. The system is an evolv-ing. one-dimensional. non-uniform cellular automaton. Each ofthe S6 cells contains a genome that represents Iu rule table;these genomes are initialized randomly. to be subjected toevolution. The board contains the following components: (I)LED indicators of cell states (top), (2) switches for manuallysetting the initial states of cells (top, below LEOs), (3) XilinxFPGA chips (below switches), (~) display and knobs for con-trolling two parameters ('time sups' and 'configurations') ofthe cellular programming algorithm (bottom left). (S) a syn-chronization indicator (middle left), (6) a dock pulse genera-tor with a manually adjustable frequency from 0.1 Hz to I MHz(bottom middle), (7) an LCD display of evolved rule tablesand fitness values obtained during evolution (bottom right).and (8) a power-supply cable (extreme left). (Note that thelatter is the system's sole external connection.)

The ultimatesystem engineers is to create more adaptive systems, in

which "adaptive" refers to a system's ability to undergo

modifications according to changing circumstances,

thus ensuring its continued functionality.

Two Examples of Current-DayBio-inspired HardwareIn the following section we will describe twodynamic systems inspired by two of the processesoutlined previously: evolution and ontogeny. Learn-ing hardware, as well as more in-depth expositions ofthe systems discussed herein can be found in therecent book by Mange and Tomassini [4].

E'I1ol11ing htmlwlIre: The Firefly mAChine. Theidea of applying the biological principle of naturalevolution to anificial systems, introduced more than

four decades ago, has experienced impressive growthin the past few years. Usually grouped under the termevolutionary algorithms or roolutionary computation,are the domains of genetic algorithms, evolutionstrategies, evolutionary programming, and geneticprogramming [6]. As a generic example of artificial

evolution we consider genetic algorithms.A genetic algorithm is an iterative procedure that

involves a constant-size population of individuals,each one represented by a finite string of symbols-the genome--encoding a possible solution in a givenproblem space. This space, referred to as the searchspace, comprises all possible solutions to the prob-lem at hand. The algorithm sets out with an initialpopulation of individuals that is generated at ran-

dom or heuristically. In every evolutionary step,known as a generation, the individuals in the currentpopulation are decoded and evaluated according tosome predefined quality criterion, referred to as thefitness function. To form a new population (the nextgeneration), individuals are selected according totheir fitness and transformed via genetically inspiredoperators, of which the most well known arecross~ ("mixing" two or more genomes to form

novel offspring) and mutation (randomly flippingbits in the genomes). Iterating this evaluation-selec-tion-crossover-mutation procedure, the genetic alg0-rithm may eventually find an acceptable solution,that is, one with high fitness.

goal iredof bio-insp

Evolutionary algorithms are common nowadays,having been successfully applied to numerous prob-lems from different domains. including optimiza-tion, automatic programming, circuit design,machine learning, economics, immune systems,ecology, and population genetics, to mention a few.

One of the recent uses of evolutionary algorithmsis in the burgeoning fidd of evolvable hardware.which involves, among others, the use of FPGAs asa platform on which evolution takes place [8, 10].The Firefly machine is one such example; our goal inconstructing it was to demonstrate a system in whichall evolutionary operations (fitness evaluation, selec-tion, crossover, and mutation) are carried out online,that is, in hardware [9, 10].

Firefly is based on the cellular automata model, adiscrete dynamical system that performs computa-tions in a distributed fashion on a spatially extendedgrid [9]. A cellular automaton consists of an array ofcells, each of which can be in one of a finite numberof possible states, updated synchronously in discretetime steps according to a local. idmtical interactionrule. The state of a cell at the next time step is deter-mined by the current states of a surrounding neigh-borhood of cells. This transition is usually specifiedin the form of a rule table, ddineating the cell's next

state for each possible neighborhood configuration.The cellular array (grid) is n-dimensional, wheren= 1, 2, 3 is used in practice. Here, we consider aone-dimensional grid, in which each cell can be inone of two states (0 or 1). and has three neighbors(itsdf, and the cells to its immediate left and right);the rule table thus comprises eight bits since there

are eight possible neighborhood configurations.Non-uniform cellular automata have also been con-sidered; for these the local update rule need not beidentical for all grid cells [9].

Based on the cellular programming evolutionaryalgorithm [9], we implemented an evolving, one-dimensional. non-uniform cellular automaton. Each

53COMMUNICA110NS OF 'ntl ACM ApriII999fVG. 42. No. 4

organization of the BioWatch.

Figure 2b. Cellular differentiation of me BioWatch.

Figure 3. Self-repair of me BioWatch. Old coordinates areshown in parenmeses.

of the system's 56 binary-state cells contains agenome that represents its rule table. These genomesare initialized at random, to be subjected to evolu-tion. The system must evolve to resolve a global syn-chronization task: upon presentation of a randominitial configuration of cellular states, the cellularautomaton must reach, after a bounded number oftime steps, a configuration whereupon the states ofthe cells oscillate between all Os and all1s on succes-sive time steps (this may be compared to a swarm offireflies, which evolve over time to flash on and offin unison). Due to the local connectivity of the sys-tem, this global behavior-which involves the entiregrid-represents a difficult task. Nonetheless, byapplying cellular programming, the system evolves(that is, the genomes change) such that the task issolved. The machine is depicted in Figure 1.

The Firefly machine exhibits complete onlineevolution; all its operations are carried out in hard-ware with no reference to an external computer.This demonstrates that evolving ware, or t'VOlW4rt',can be constructed [9]. Such evolware systems per-

mit enormous gains in execution speed (for exam-ple, Firefly runs 1000 times Mer than a simulationon a high-performance workstation). While thesynchronization taSk is not a real-world application~d was selected to act as a benchmark problem forour evolware demonstration, Firefly does open upinteresting avenues for future research. Evolwaremachines that operate in an autonomous manner

can be used to construct autonomous mobile robots,as well as for the constructionof controllers for noisy,changing environments [10].

Ontogenetic htlrtlware:11Je Bio Watch. The Bio Watchis one of the applicationsdesigned as part of the Embry-onics (embryonic electronics)project, whose final objective isthe devdopment of very large-scale integrated circuits, whichare capable of self-repair andself-replication [3, 4]. Thesetwo bio-inspired properties,characteristic of the livingworld, are achieved by trans-

posing cert3in features of cellu-lar organization in nature ontothe tWO-dimensional world ofintegrated circuits in silicon.is an artificial "organism" .

to count (&om 00 to 59) and00 to 59); it is thus a modulo-3600 counter. This

The Bio WatchmInutes

organism is one-dimensional and is comprised offour cdls with identical physical connections and anidentical set of resources. The organization is multi-cellular (as with living beings), with each cell realiz-ing a unique function, described by its gene (seeFigure 2a).

The genome is the set of all the genes of theBio Watch, and each gene is a sub-program, charac-terized by a set of instructions and by its horizontalcoordinate X Storing the whole genome in each cellrenders the cell universal, that is, capable of realizingany gene of the genome. This is another bio-inspired property: each of our (human) cells alsocontains the entire genome, though only part of it isused (for example, liver cells do not use the samegenes as muscle cells). Depending on its position inthe organism, each cell interprets the genome,extracting and executing the gene that configures it.The Bio Watch thus performs what is known in biol-ogy as cellular differentiation (see Figure 2b).

Self-repair of an artificial organism allows partialreconstruction of the original device in case of a

4. Self-replication of me BioWatch.Figure

Flsure 5. An eight-cell BioWatCh. (Note: while our long-term objective is me design of very large-scale Integrated cir.cuits, each BioWatCh cell is currendy Implemented in an ActeI1020 FPGA orcutt and embedded wlmln a smail plasdc box

mat serves as a d.nonstration module.)

minor fault. In the Bio Watch, each cell performs oneof two specific tasks: a modul0-6 or a modulo-IOcount (see Figure 2a). Self-repair can be attained bydynamically reconfiguring the task executed by someof the cells. In order to implement this process, theBio Watch must have as many spare cells to the rightof the array as there are faulty cells to repair (thereare four spare cells in the example of Figure 3). Self-repair is achieved by bypassing the faulty cell andshifting to the right all or part of the original cellu-lar array. The new coordinates, thus defined, lead tothe dynamic reconfiguration of the task performedby the cell (modulo-6 or modulo-tO count).

Self-replication of an artificial organism com-pletdy reconstructs the original device in case of amajor fault. In the Bio Watch, the self-replicationprocess rests on two assumptions: (I) there exists asufficient number of spare cells to the right of thearray (four in our example), and (2) the calculationof the coordinates produces a cycle (X=I -+2 -+3-+4 -+ 1 in Figure 4). As the same pattern of coordi-nates produces the same pattern of genes, sdf-repli-cation can be easily accomplished if themicroprogram of die genome, associated with thehomogeneous network of cells. produces severalinstances of the basic pattern of coordinates.

, counter With a larger number of-- ~c, celIs it becomes possible to

m add the ~tensions needed for

mr n;r a practIcal use of the

. Bio Watch: preserving the cur-J . rent time while self-repair is

, being effected, and settingand resetting the time (see

Figure 5). It is also quite easy to introduce additionalfunctions such as computing the dare, keeping trackof mc day of the week, and handling leap years.

The Future of Bio-inspired HardwareAccording to current evolutionary theory. life onearth originated about 4 billion years ago. butevolved slowly for about 3.5 billion years; this isknown as the pre-Cambrian period Then. in a rela-tively shon span of a few million years. beginningabout 550 million years ago-the Cambrianperiod-a vast array of multicellular life abrupdyemerged. This period is also known as the Cambrianexplosion. The first advanced cell. which served as abasis for all higher life forms (eukaryotes). came intoexistence when some kind of host acquired as sym-biotic parmer a number of smaller components;these latter-known as organelles--originated as&ee-living bacteria. The host and the organellesentered into an endosymbiotic relationship. that is.symbiosis in which a symbiont dwdls within thebody of its symbiotic panDer. (The endosymbiotictheory was put forward by Lynn Margulis in theearly 1970s [5]; though controversial at the time. ithas gained acceptance during the intervening years.)

The evolution of microprocessors. as effected byengineers (the "hand of Goo" discussed earlier inthis anicle). seems to have followed a similar path.Units that were originally "free-~iving. .. situated out-

side the processor. were moved into it: the memorymanagement unit (MMU). the floating point unit(FPU). and the cache memory. all of which were atfirst separate units. are found today within theprocessor itself. The latest development involves theaddition of reconfigurable on-chip surfaces; forexample. Triscend has recently announced a micro-processor chip with a 65% reconfigurable surface.with only 35% of the surface serving as a classicalcontroller to manage the chip's operation [11].

Docs this new "organelle" ponend the upcoming"Cambrian explosion" of adaptive. bio-inspiredhardware? The trend toward a merger of the classi-cal-processor industry with the configurable-com-puting one will probably not only continue. butintensify in the future. While FPGAs currendy rep-resent only a small niche within the computer-hard-

COMMUNlCAnota Of 1HI ~ .. 1999fVg1. 41. No. 4 5 5

ware industry, this new development might elevatethem to ubiquity. These new processors will findtheir way to every desktop, providing a base foradaptive machines. This might lead to an explosionof adaptive-hardware applications--computers thatcan change not only their inner workings but indeedtheir inner structure in response to changes in theenvironment.

The environment in question includes everythingthat the configurable processor contacts: other inter-nal units within the computer, the user (or users),and the network. Future computers might thus beshipped only partially designed, to be then subjectedto evolution in the field-that is, on the desktop.Furthermore, they might repair themsdves upon suf-fering damage, as the Bio Watch does. Ultimately,computers might evolve to improve their own per-formance-mat is, they might go from the 8086epoch to the 80286 epoch (as noted previously)without the intervention of a human engineer.

The road ahead is, however, still fraught withmany obstacles. While the dectronic suppon forincreased adaptability might exist, there are stillmajor unresolved issues, some of which we discusshere. For one thing, we have still not mastered thistechnology: configuring a configurable processor isat least as hard (if not harder) than programming aclassical processor, and the available tools for config-uration are far behind those for programming.

The application of bio-inspired methodologiesmight serve to offset this problem by obviating theneed for human design altogether. One mustremember, however, that natural evolution hasrequired a huge amount of resources (in terms oftime and space), and most of the avenues exploredhave been dead ends (there are many more extinctspecies than surviving ones). The ideal path mightthus be somewhere between complete design andcomplete autonomy: initial human design followedby funher autonomous adaptation.

Another major issue is that of hierarchy. Naturehas evolved demental building blocks, out of whichshe constructs more complex systems: moleculescombine to form cells, cells combine to form tissues,tissues combine to form organisms, and organismscombine to form societies. Such a layered approachhas not escaped computing practitio~ers in th~ir

repair to take place within the cell as well as outsideit; the resulting system is more efficient in its use ofchip surface {only extracellular self-repair was delin-eated earlier in this article; for a full account therader is referred to [4». Note that such hierarchies,which are of major concern within the configurable-computing community, are currendy engineeredinto our artificial systems whereas in nature theyhave emerged through evolution.

Despite these obstacles, the future seems pregnantwith possible applications for adaptive hardware. Inthe end it will probably be the enabling technologythat furnishes the impetus for progress in this area.The evolution of chip technology might well be theultimate harbinger of evolving chips. D

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Iht Pmm.oatioft of F--- RACIS ift Iht SITUggie.for Lift. John Murray.London. 1859.

2. Dawkins. R. Tbt B/inJ W4lth-m. W.W. Norton and Company.New York. 1986.

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Univeniwres Romandes, Lausanne, Swirzeriand, 1998.5. Margu1ia, L Symbiosis and evolution. Scimtific ArIUricAII225, 1 Quly

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P~rez-Uribe, A. Static and dynamic conflgurable systemS. IEEE TNN-4ai#N Oft CDmputm. To appear.

8. Sancllez. E. and Tomassini, M.. Eds. Towards evolvable hardware. LN-lur.-ND#S in c-",.,.Scima, 110£ 1062, Springer-Verlag, Heidelberg,1996.

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10. Sipper. M., Sancllez. E.. Mange. D.. Tomassini. M., perez-Uribe, A.,and Stauffer. A. A phyiosenetic, ontogenetic, and epigenetic view ofbio-inspired hardware systems. IEEE TranstlCtiD1U Oft Ewhlti#II41JCompllt4tWft I, 1 (Apr. 1997). 83-97.

11. Turley, J. Trisand ES reconfigures microcontroUers. MimIproe-&pm 12, 15 (1998), 12-13.

12. Villasenor. J. and Mangione-Smith, W.H. Conflgurable compucing.Scimlific Alllm'ca 276. 6 Qune 1997), 54-59.

MOSHE SIPPER (Moshe.Sipper@ep8.ch) is a senior researcher at

the Swiss Federal Institute ofTcchnology in Lausanne, Swirzerland;

Is/www .ep8.chDANIEL MANGE (Daniel.Mange@ep8.ch) is a professor at the

Swiss Federal Institute ofTcchnology in Lausanne, Swiaerland;

Is/www.epfl.chEDUARDO SANCHEZ (Eduardo.Sanche'L@eptlch) is a pro~r at

the Swiss Federal Institute ofTechnolO2V in Lausnne, Swi!7.erland;