Search

Many different physical substrates can support complex computation. This is particularly apparent when considering human made and biological systems that perform similar functions, such as visually guided navigation. In common, however, is the need for good physical design, as such designs are smaller, faster, lighter, and lower power, factors in both the jungle and the marketplace. Although the physical design of man-made systems is relatively well understood, the physical design of biological computation has remained murky due to a lack of detailed information on their construction. The recent EM (electron microscope) reconstruction of the central brain of the fruit fly now allows us to start to examine these issues. Here we look at the physical design of the fly brain, including such factors as fan-in and fanout, logic depth, division into physical compartments and how this affects electrical response, pin to computation ratios (Rent's rule), and other physical characteristics of at least one biological computation substrate. From this we speculate on how physical design algorithms might change if the target implementation was a biological neural network.

The 2021 International Symposium on Physical Design lifetime achievement award goes to Dr. Louis K. Scheffer for his outstand contributions to the field. This autobiography in Lou's own words provides a glimpse of what has happened through his career.

Fruit flies (Drosophila melanogaster) are small insects, with correspondingly small power budgets. Despite this, they perform sophisticated neural computations in real time. Careful study of these insects is revealing how some of these circuits work. Insights from these systems might be helpful in designing other low power circuits.

Electronic and biological systems both perform complex information processing, but they use very different techniques. Though electronics has the advantage in raw speed, biological systems have the edge in many other areas. They can be produced, and indeed self-reproduce, without expensive and finicky factories. They are tolerant of manufacturing defects, and learn and adapt for better performance. In many cases they can self-repair damage. These advantages suggest that biological systems might be useful in a wide variety of tasks involving information processing. So far, all attempts to use the nervous system of a living organism for information processing have involved selective breeding of existing organisms. This approach, largely independent of the details of internal operation, is used since we do not yet understand how neural systems work, nor exactly how they are constructed. However, as our knowledge increases, the day will come when we can envision useful nervous systems and design them based upon what we want them to do, as opposed to variations on what has been already built. We will then need tools, corresponding to our Electronic Design Automation tools, to help with the design. This paper is concerned with what such tools might look like.

It may seem premature to worry about the design of of living systems when we do not yet fully understand their operation. However, the path to understanding is clear, and design will inevitably follow. It's not too early to think about how this might be structured.

Overview Two years ago, DAC-2001 attendees enjoyed a thrilling debatepanel, “Who’s Got Nanometer Design Under Control?”, pitting sky-is-falling Physics die-hards against not-to-worry Methodology gurus. Then, the DAC audience overwhelmingly voted the match for the Methodologists. Now, we've just gone through the biggest business downturn in the industry's history, and we're hearing more and more about chip failures due to 130nm physical effects. Both physics and economics are a lot worse than we thought two years ago. Where are those simple, correct-by-construction methodologies for signal integrity, power integrity, low-power, etc. that we were promised? Were we bamboozled by glib promises from those Methodologists? In this session, we bring back the panelists from two years ago, not for another debate, but to hear well-reasoned perspectives on how to prioritize spending to address nanometer design challenges. Yes, methodology can solve any problem – but now we want to know which problems, in what priority order, at what cost. The panel will address the following questions. • What are the economic impacts and significance of the key nanometer design challenges, relative to each other? • Which nanometer design problems merit responsible R&D investment, in what amounts and proportion? • What is the likelihood of success, both near-term and longterm, in solving key nanometer design challenges? • Where will the answers come from? To keep the discussion very concrete, each panelist will be given a $100 budget, and must defend their allocation of this budget to attack various design problems. Where should the $100 be spent? The audience will determine the best-reasoned allocation, and the winning panelist keeps all the money.