L22: Wrap-up

And now we’ve reached the end of 6.004. Looking back, there are two ways of thinking about the material we’ve discussed, the skills we’ve practiced, and the designs we’ve completed.

Starting at devices, we’ve worked our way up the design hierarchy, each level serving as building blocks for the next. Along the way we thought about design tradeoffs, choosing the alternatives that would make our systems reliable, efficient and easy to understand and hence easy to maintain.

In the other view of 6.004, we created and then used a hierarchy of engineering abstractions that are reasonably independent of the technologies they encapsulate. Even though technologies change at a rapid pace, these abstractions embody principles that are timeless. For example, the symbolic logic described by George Boole in 1847 is still used to reason about the operation of digital circuits you and I design today.

The power of engineering abstractions is that they allow us to reason about the behavior of a system based on the behavior of the components without having to understand the implementation details of each component.

The advantage of viewing components as “black boxes” implementing some specified function is that the implementation can change as long as the same specification is satisfied. In my lifetime, the size of a 2-input NAND gate has shrunk by 10 orders of magnitude, yet a 50-year-old logic design would still work as intended if implemented in today’s technologies.

Imagine trying to build a circuit that added two binary numbers if you had to reason about the electrical properties of doped silicon and conducting metals. Using abstractions lets us limit the design complexity at each level, shortening design time and making it easier to verify that the specifications have been met. And once we’ve created a useful repertoire of building blocks, we can use them again and again to assemble many different systems.

Our goal in 6.004 is to demystify how computers work, starting with MOSFETs and working our way up to operating systems. We hope you’ve understood the engineering abstractions we’ve introduced and had a chance to practice using them when completing the design problems offered in the labs.

We also hope that you’ll also understand their limitations and have the confidence to create new abstractions when tackling new engineering tasks. Good engineers use abstractions, but great engineers create them.

6.004 is an introductory course that only touches upon the basic principles used at each level of the design hierarchy. If a particular topic struck you as especially interesting, we hope you’ll seek out a more advanced course that will let you dig deeper into that engineering discipline. Hundreds of thousands of engineers have worked to create the digital systems that are the engines of today’s information society. As you can imagine, there’s no end of interesting engineering to explore and master - so roll up your sleeves and come join in the fun!

What will be the engineering challenges of tomorrow? Here are a few thoughts about how the future of computing may be very different than the present.

The systems we build today have a well-defined notion of state: the exact digital values stored in their memories, produced by their logic components, and traveling along their interconnect. But computation based on the principles of quantum mechanics may allow us to solve what are now intractable problems using states described not as collections of 1’s and 0’s, but as interrelated probabilities that describe the superposition of many states.

We’ve built our systems using voltages to encode information and voltage-controlled switches to perform computation, using silicon-based electrical devices. But the chemistry of life has been carrying out detailed manufacturing operations for millennia using information encoded as sequences of amino acids. Some of the information encoded in our DNA has been around for millions of years, a truly long-lived information system! Today biologists are starting to build computational components from biological materials. Maybe in 50 years instead of plugging in your laptop, you’ll have to feed it :)

Instead of using truth tables and logic functions, some computations are best performed neural networks that operate by forming appropriately weighted combinations of analog inputs, where the weights are learned by the system as it is trained using example inputs that should produce known outputs. Artificial neural nets are thought to model the operation of the synapses and neurons in our brains. As we learn more about how the brain operates, we may get many new insights into how to implement systems that are good at recognition and reasoning.

Again using living organisms as useful models, programming may be replaced by learning, where stimulus and feedback are used to evolve system behavior. In other words, systems will use adaptation mechanisms to evolve the desired functionality rather than have it explicitly programmed.

This all seems the stuff of science fiction, but I suspect our parents feel the same way about having conversations with Siri about tomorrow’s weather.

Thanks for joining us here in 6.004. We’ve enjoyed presenting the material and challenging you with design tasks to exercise your new skills and understanding. There are interesting times ahead in the world of digital systems and we can certainly use your help in inventing the future!

We’d welcome any feedback you have about the course so please feel free leave comments in the forum. Good bye for now... and good luck in your future studies...