DOI: http://dx.doi.org/10.7551/978-0-262-33027-5-ch097 Artificial life programming in the robust-first attractor David H. Ackley1 and Elena S. Ackley2 1University of New Mexico, Albuquerque, NM 87131 2Ackleyshack LLC, Placitas, NM 87043 [email protected] Downloaded from http://direct.mit.edu/isal/proceedings-pdf/ecal2015/27/554/1903815/978-0-262-33027-5-ch097.pdf by guest on 27 September 2021 Abstract Although the SDA robustness and security properties are dubious, and its scalability is rapidly dwindling, it has Despite mounting awareness of the liabilities of determinis- been so dominant that alternatives may seem unthinkable. tic CPU and RAM computing, across industry and academia One might imagine that fields like fault tolerance (IEEE, there remains little clear vision of a fundamental, general- 2013, e.g.,) or probabilistic algorithms (Karp, 1991) fall purpose alternative. To obtain indefinitely scalable computer architectures offering improved robustness and security, we outside the SDA, but by ‘virtually guaranteeing’ determin- have advocated a realignment of the roles of hardware and istic execution, they actually entrench it. The same is software based on artificial life principles. In this paper we true of many other non-traditional but still deterministic propose an active media computational abstraction to under- models, such as synchronous cellular automata (von Neu- lie such a hardware-software renegotiation. The active me- mann and Burks, 1966; Ulam, 1950; Toffoli and Margolus, dia framework is much in the spirit of probabilistic cellular automata, but designed for indefinite scalability and serious 1987, e.g.), data flow machines and systolic arrays (Borkar programmability, rather than simplicity and analytic tractabil- et al., 1988; Budzynowski and Heiser, 2013, e.g.), and ity. We discuss active media programming techniques based asynchronous circuit-level techniques such as GALS and on living systems principles, and present anecdotal data from RALA (Kishinevsky et al., 2007; Gershenfeld et al., 2010). sample programs to introduce a new programming language called ulam, that we are developing as an underlying lan- Probabilistic cellular automata (PCA) (Grinstein et al., guage for active media. 1985; Agapie et al., 2014, e.g.) do go decisively beyond determinism, and they are general enough to embrace the kind of models we explore—but their motivations and meth- Introduction ods are sharply divergent from the present effort. PCA work often presumes simple and stylized noise models, and As the hegemony of CPU and RAM declines, for the first proceeds—preferably by formal analysis—to derive insights time in decades significantly new computer architectures are into equilibrium distributions and other system properties. appearing—from the nothing-but-net neural architecture of But when such research begins by postulating a state transi- IBM’s TrueNorth (Merolla et al., 2014), to the memristor- tion matrix, the small matter of actual PCA programming is driven flat parallelism of HP’s “The Machine” (Williams, silently assumed away. Yes, the transition matrix is a pow- 2014). With the potential on the horizon for a major evo- erfully general device; no, you don’t want to program in it. lutionary transition in computer architecture, it is an oppor- tune time to reconnect with first principles before shortlist- Recently, there have been some serious programming re- ing successors. The result of such a process, we believe, will search efforts that, while remaining mostly traditional, do be the recognition of artificial life as a (perhaps the) major explicitly abandon determinism and accept some small out- force driving future architectural innovation. put errors—often with the motivation of increased parallel efficiency (Cappello et al., 2009; Elliott et al., 2014; Mis- Escape from the SDA ailovic et al., 2013; Renganarayana et al., 2012, e.g.). We cheer all such efforts but worry they may fail to gain traction Serial deterministic computing based on CPU and RAM is a because their incremental practicality leaves them struggling vast attractor, a valley deep and wide, in a notional space of up the sides of the SDA valley, with all the downhill direc- all possible models of computation. This Serial Determin- tions behind them. istic Attractor (SDA) is laced with interlocking design deci- sions surrounding its core demand for logical correctness— Colonize the RFA which allows the inherent fragility of extremely efficient software to be masked by extremely reliable hardware. Until There is at least one fundamental alternative, which we here a bug, or an attacker, appears. call the Robust-First Attractor (RFA), in the space of all pos- David H. Ackley, Elena S. Ackley (2015) Artificial life programming in the robust-first attractor. Proceedings of the European Conference on Artificial Life 2015, pp. 554-561 sible models of computation. We have been breaking trail in Stefanovic, 2003, e.g.); it is already possible in electron- the RFA for some time (Ackley and Cannon, 2011; Ack- ics (Ackley et al., 2013; Ganapati, 2009). ley, 2013b; Ackley et al., 2013; Ackley, 2013a; Ackley and Small, 2014a), and can report it is strikingly unlike the SDA, A new deal for hardware and software but at least as vast: It is a natural way to understand the com- Clearly, compared to an SDA computer architecture, the ac- putational properties of living systems, which have always tive media model represents a very different division of labor made do without the luxury of deterministic execution. between hardware and software, as large blocks like ‘pro- Life fills space, as long as suitable resources are available; cessor’ and ‘memory’ and ‘bus’—and their floorplanning— every RFA architecture must do the same, and that core de- are placed largely under software control. This refactoring mand for indefinite scalability is surrounded by interacting will presumably incur a hardware price-complexity penalty design decisions often deeply complementary to the SDA’s. something like FPGA vs ASIC or worse—but that, in turn, A von Neumann machine by itself simply isn’t an RFA ar- may be more than offset by enabling new optimizations akin Downloaded from http://direct.mit.edu/isal/proceedings-pdf/ecal2015/27/554/1903815/978-0-262-33027-5-ch097.pdf by guest on 27 September 2021 chitecture; it is just incomplete, and thus unevaluatable, until to RISC vs CISC, combined with the hair-down liberation a method is defined for tiling unbounded space with it. of merely best-effort hardware determinism. Most software-based artificial life models are designed So, while the programmable active media framework2 is 1 to run on single von Neumann machines. Unsurprisingly, likely a splendid deal for hardware, it may seem a brutal one- therefore, the properties of such models typically depend two punch for software, stunned by nondeterminism from critically on deterministic execution, as typified by the ut- below then flattened by expanded mission responsibilities ter collapse of constructs in Conway’s game of life when from above. We take that added software engineering com- facing even mild asynchrony (Bersini and Detours (1994); plexity as underlying the “hard to program” objection lev- see also Beer (2014)). eled against our approach in a discussion of a very interest- Determinism is a property of the small and the fragile; it ing spatial and parallel—though apparently deterministic— is fundamentally misaligned with living systems. It warps model of computation (Budzynowski and Heiser, 2013). our expectations; it’s time to move on. But here’s the thing: On the one hand, the software en- gineering job should be harder, because its relative simplic- Programmable active media ity was purchased with precisely those von Neumann ma- SDA models are well-suited to implementation in passive, chine features—a single processing locus, uniform passive “cold” materials, where uniformity rules, change is rare, and memory, reliability all on hardware—that led to its Achilles’ free energy is expensive—conditions where, indeed, living heels of unscalability and unsecurability. Serial determin- systems may survive but will rarely thrive. However, some ism was a simple, sensible starting point, but software en- environments are diverse in space, dynamic in time, and en- gineering and many related fields have emerged since von ergetically rich, bountiful, like a rain forest or a sunny day at Neumann’s time, and we now know quite a bit about con- the shore. We abstract such circumstances into active media structing, managing, and evolving complex systems. Look- computational models—unbounded spatial architectures in ing back from the RFA, for software still to be demanding which each discretized location performs logical state tran- general pointers and flat RAM and cache coherent global sitions based on its local neighborhood, but with uncertain determinism seems like clutching blankie. The future will and variable frequencies and only limited reliability. arrive anyway. An active medium can change spontaneously and is inher- That said, and on the other hand, software’s big promo- ently nondeterministic. In a programmable active medium tion becomes less terrifying as we get down to work, be- we get to pick its state transition function—to specify, up cause, like hope from Pandora’s box, “best effort” wafts up- to reliability limits, that certain neighborhood patterns shall wards from the nondeterministic hardware into the software stay constant like memory, say, while others produce transi- as well. As a system component, we’ll do our best with what tions like a processor or a data transport, or, indeed, act like we’ve got and what we get, but if things go really wrong, different types of hardware at different moments. The state we can simply delete ourselves and let our kin cover for us. transition function we supply is executed asynchronously in Correctness and robustness are measured by degrees and cir- parallel across the medium, avoiding overlapping state tran- cumstances in living systems; in the RFA they are highly sitions, again, with good but not guaranteed reliability.