Afternoon: Bio/Medical DA
(Chair:
Sankar Basu, NSF)
-----------------------------------------
Synthetic Biology: A New Application Area
for Design Automation Research
Chris Myers - University of Utah
EDA tools have facilitated the design of ever more complex integrated
circuits each year. Synthetic biology would also benefit from the
development of genetic design automation (GDA) tools. Synthetic
biology has the potential to help us produce drugs more economically,
metabolize toxic chemicals, and even modify bacteria to hunt and kill
tumors. There are, however, numerous challenges to design the
genetic circuits used in these applications. First, existing GDA tools
require biologists to design and analyze genetic circuits at the
molecular level, roughly equivalent to the layout level for electronic
circuits. Another serious challenge is that genetic circuits are
composed of very noisy components making their behavior more
asynchronous, analog, and non-deterministic in nature. New GDA
research is necessary to address these challenges. Interestingly,
future electronic circuits may soon also face many of the same
challenges which opens up the very intriguing idea that this research
may in the future also be utilized to produce more robust and power
efficient electronic circuits. This talk will briefly describe
our first steps in the development of iBioSim, a GDA tool that supports
higher levels of abstraction. This talk will also present some of
the important theoretical and computational research problems in this
area that will need to be addressed.
EE
and neural biology - twins separated
at birth?
Lou Scheffer, Howard Hughes
Medical Institute
There is great potential for ideas
developed for EE to help the
upcoming science of quantitative neural biology. Like EE, the
most accurate biological models involve differential equations, both
linear and non-linear. Also, like EE, the most accurate
simulations involve direct solutions of these equations in a manner
similar to SPICE. Furthermore, like EE, only some of the many
variables affect the output - for example, mammalian neurons are give a
response when the voltage at a single point, the trigger spot,
exceeds a certain value. The value at this spot is a complex
function of the many inputs of the neurons. Perhaps, as in
digital design, there are simpler ways to figure out the value at this
point without solving for all values at all intervening points.
If this can be done (and it is not yet completely clear which portions
of the response must be preserved) then perhaps model order reduction
techniques from EE could playa part in biology. As another
example, the neuron to neuron transmission is always statistical, with
a small number of vesicles released, each with a (relativley) small
number of transmitter molecules. This is recieved by a small
number of ion channels that stay open for a (statistical) value of
time. Although some responses invlove large numbers of
vessicles, molecules, or synapses, these are the exception rather than
the rule. Thus statistical methods developed for IC timing may be
a good starting point for biological calculations. These are just
two examples - many more are possible and probably necessary.
About the speaker: Lou Scheffer is a traitor to the field of EE,
having recently succumbed to the dark side and accepted a position at
the Howard Hughes Medical Institute, studying the construction and
wiring of the brain.
Computing
with Things Small, Wet, and
Random: Design Automation for
Nanoscale Technologies and Biological Processes
Marc Riedel,
Univ. of MN
Abstract: This talk will discuss
techniques for synthesizing circuits and biological systems that are
characterized by uncertainty in the way that they are wired or
that they execute. We adopt a novel view of computation: instead of
transforming definite inputs into definite outputs, circuits and
biological systems transform probability values into
probability values. The computation is random at the level
of bits or protein-protein reactions; nonetheless, in the
aggregate, it becomes exact and robust, since the accuracy depends only
on the statistical distributions. The talk will present
novel circuit constructs that are analog in character but
based on digital components. Also, it will present
biological constructs that are digital in character in the sense that
they deliver robust outcomes. We propose a bio-design automation flow
in which synthesis first is performed at a conceptual
level, in terms of abstract biochemical reactions -- a
task analogous to technology-independent logic synthesis
in circuit design. Then the results are mapped onto specific
biochemical reactions, selected from libraries -- a task analogous to
technology mapping in circuit design. Our method targets the universal
DNA substrate developed by Erik Winfree's group at Caltech
as the experimental chassis.
Biography:
Marc Riedel has been an Assistant Professor of Electrical and Computer
Engineering at the University of Minnesota since 2006. He is also a
member of the Graduate Faculty in Biomedical Informatics and
Computational Biology. He has held positions at Marconi Canada, CAE
Electronics, Toshiba, and Fujitsu Research Labs. He received his Ph.D.
and his M.Sc. in Electrical Engineering at Caltech and his B.Eng. in
Electrical Engineering with a Minor in Mathematics at McGill
University. His Ph.D. dissertation titled "Cyclic Combinational
Circuits" received the Charles H. Wilts Prize for the best doctoral
research in Electrical Engineering at Caltech. His paper "The Synthesis
of Cyclic Combinational Circuits" received the Best Paper Award at the
Design Automation Conference. He is a recipient of the NSF CAREER Award.
Self-Assembly
for the More-Than-Moore Era
Babak Parviz,
Univ of Washington
Abstract: Two trends dominate the
electronics industry today: miniaturization and integration of multiple
functions into single microsystems. As further reduction in size of
electronic devices by extending the current top-down fabrication
techniques becomes exceedingly challenging, and as the need for the
integration of an ever increasing number of small components into a
system becomes evident, new approaches are needed to maintain the pace
of progress in the sector. A candidate approach for building small and
complex systems is to use self-assembly. Self-assembly is the process
of spontaneous organization of components into a higher structure. This
talk reviews some of the recent work at the University of Washington to
engineer and use self-assembly across the size scales for building
structures and functional devices. Examples covered include hybrid
organic-inorganic devices, structures made using genetically engineered
peptides, and microsystems produced with self-assembly including a
functional contact lens.
-----------
Summary of the 2009
NSF-Design Automation
Workshop "Electronic Design Automation Past, Present, and Future"
Sankar Basu, Robert Brayton, Jason
Cong
Abstract: Electronic Design Automation (EDA) has been an
immensely successful field, helping to manage the exponential increase
in our capability to implement integrated circuits that currently
incorporate billions of transistors. At the same time, it fostered and
used theories in computation and modeling, successfully combining
theory and practice. EDA has completely transformed the way that
electronic engineers design and manufacture integrated circuits. It was
one of the earliest to engage in inter-disciplinary collaboration,
where the computer scientists and engineers in EDA successfully
collaborated with electrical engineers, physicists, chemists,
theoretical computer scientists, applied mathematics and optimization
experts, and application domain specialists. This workshop was
organized to 1) reflect on the success of EDA to see if its practice
can influence other fields of computer science, and if its methodology
can be applied to other application domains, and 2) to review the
progress made under the National Design Initiative and evaluate what
new directions and topics should be added to the Initiative.
This report contains an overview of the EDA area, its funding history,
a discussion of some major challenges for the future, related emerging
technologies and how EDA experience may help in developing these
technologies, educational aspects and challenges, EDA's relation with
CS theorists and how this collaboration can be resurrected, and finally
in Section 8, a series of recommendations on what might be done to
promote EDA and help with the serious challenges it faces in the
future. The recommendations are divided into 1) promoting research, 2)
supporting educational programs, and 3) encouraging enhanced
collaboration with industry.
Gaps and Opportunities in EDA
David
Kung, IBM Research
Abstract:
As technology
and applications
evolve, design tools and methodology evolve as well to adapt to the
changes. But when disruptive technology or application emerges, tools
and methodology will not be able to keep up and gaps are created. Some
species of tools will become extinct and new species will rise up to
fill the gaps. In this presentation, I will talk about these gaps with
emphasis on high performance chip design. Some on-going and potential
disruptions I will focus on are the emergence of manycore chips,
the unreliable devices, the lithography challenges beyond 22 nm, the
increased level of integration, and the end of CMOS. The impact and
demand on CAD tools will be discussed.
EDA
Investment
Lucio Lanza, Lanza techVentures
New Rules for Building a Successful EDA
Business
Rajeev Madhavan,
Magma Design
Automation
Abstract: Over the last 10 years, a lot has changed in the EDA
market. It used to be that with an idea for a slight tool
improvement, a small group of software engineers and some venture
funding, you could start a company and within a few years sell it to
one of the top players in the EDA industry. As designs became more
complex, the interdependencies of timing, area, power, turnaround time
and other criteria created the need for a more holistic approach. To
develop such solutions, more manpower, truly innovative technology and
a larger investment are required. Today, large venture capitalists
aren't investing in EDA, and the top EDA companies have dramatically
scaled back their acquisition strategies. In this presentation, Rajeev
Madhavan will share his experiences as a serial entrepreneur and CEO.
He'll present case studies on two very different acquisitions and will
describe what it takes to build a successful EDA business today.
Poster session:
|
Affiliation |
Supervisor |
Presenter
|
Topic
|
|
Colorado State U |
Prof. Sudeep Pasricha |
Shirish Bahirat |
Hybrid Nano-photonic-electric On-chip Comm Architecture |
|
Michigan Tech |
Prof. Hu Shiyan |
Jia Wang |
Timing driven buffer insertion for carbon nano-tube
interconnect and copper interconnect |
|
U of Michigan |
Prof. Igor Markov |
Jarrod Roy |
Hardware IP protection and anti-piracy |
|
U of Cincinnati |
Prof. Ranga Vemuri |
Hao Xu |
Aggressive Runtime Leakage Control in DSM CMOS |
|
U of Utah |
Prof. Priyank Kalla |
Christopher Condrat |
Logic Synthesis using optical devices |
|
U of Virginia |
Prof. Mircea R. Stan |
Adam Cabe |
Reliability Sensor Distribution using Scan Chain Insertion |
|
Zhenyu Qi |
MSN: Memory Sensor for NBTI |
|
UC Berkeley |
Prof. Jaijeet Roychowdhury |
Chenjie Gu |
Non-linear projection based model order reduction for
circuits and bio-chemical systems |
|
UCLA |
Prof. Jason Cong |
Guojie Luo |
3D Physical design flow and 3D physical hierarchy exploration |
|
Yi Zhou |
Parallel Multi-level Analytical Global Placement on GPU |
|
UIUC |
Prof. Martin Wong |
Yan Tan |
Printed Circuit Board Routing Is Calling for DA Help |
|
Hongbo Zhang |
Process-Aware 1-D Standard Cell Design |
|
Virginia Tech |
Prof. Patrick Schaumont |
Abhranil Maiti |
Hardware enabled methodologies for protection of software IP |
|
Prof. JoAnn Paul |
Mwaffaq Otoom |
Defining and designing Multicore workload specific processors |
|
UT Austin |
Prof. David Pan |
Duo Ding |
CAD Optimizations for On-chip Integration of Silicon
Nano-photonics |
|
UC Riverside |
Prof. Sheldon Tan |
Thom Jefferson Eguia |
General Behavioral Thermal Modeling and Characterization for
Multi-core Microprocessor Design |
|
Penn State U |
Prof. Xie Yuan |
Xiangyu Dong |
System Level Performance, Energy and Area Estimation for
PC-RAM Array |
|
Université Bretagne Sud |
Prof. Philippe Coussy |
Ghizlane LHAIRECH-LEBRETON |
Low Power High Level Synthesis for Designing DSP Applications
on FPGA |
The CANDE workshop closes after lunch on Saturday - ICCAD starts Sunday
evening