hrge grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable D S P chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the D S P sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.

How humans and technology evolve together in a creative partnership. In this book, Edward Ashford Lee makes a bold claim: that the creators of digital technology have an unsurpassed medium for creativity. Technology has advanced to the point where progress seems limited not by physical constraints but the human imagination. Writing for both literate technologists and numerate humanists, Lee makes a case for engineering—creating technology—as a deeply intellectual and fundamentally creative process. Explaining why digital technology has been so transformative and so liberating, Lee argues that the real power of technology stems from its partnership with humans. Lee explores the ways that engineers use models and abstraction to build inventive artificial worlds and to give us things that we never dreamed of—for example, the ability to carry in our pockets everything humans have ever published. But he also attempts to counter the runaway enthusiasm of some technology boosters who claim everything in the physical world is a computation—that even such complex phenomena as human cognition are software operating on digital data. Lee argues that the evidence for this is weak, and the likelihood that nature has limited itself to processes that conform to today's notion of digital computation is remote. Lee goes on to argue that artificial intelligence's goal of reproducing human cognitive functions in computers vastly underestimates the potential of computers. In his view, technology is coevolving with humans. It augments our cognitive and physical capabilities while we nurture, develop, and propagate the technology itself. Complementarity is more likely than competition.

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Should digital technology be viewed as a new life form, sharing our ecosystem and coevolving with us? Are humans defining technology, or is technology defining humans? In this book, Edward Ashford Lee considers the case that we are less in control of the trajectory of technology than we think. It shapes us as much as we shape it, and it may be more defensible to think of technology as the result of a Darwinian coevolution than the result of top-down intelligent design. Richard Dawkins famously said that a chicken is an egg's way of making another egg. Is a human a computer's way of making another computer? To understand this question requires a deep dive into how evolution works, how humans are different from computers, and how the way technology develops resembles the emergence of a new life form on our planet. Lee presents the case for considering digital beings to be living, then offers counterarguments. What we humans do with our minds is more than computation, and what digital systems do—be teleported at the speed of light, backed up, and restored—may never be possible for humans. To believe that we are simply computations, he argues, is a “dataist” faith and scientifically indefensible. Digital beings depend on humans—and humans depend on digital beings. More likely than a planetary wipe-out of humanity is an ongoing, symbiotic coevolution of culture and technology.

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