Visiting EnyAC
by Ivan Ukhov
Embedded Systems Laboratory
Linköping University
2015
Introduction
In the year of 2015, I paid a visit to a research group located at Carnegie Mellon
University (CMU), Pittsburgh, PA, USA. The group in question is called EnyAC—
the name is derived from ENIAC, the first general-purpose computer—and I
was honored to be a part of EnyAC from early May till late August, which was
just the perfect timing weather-wise. In this short essay, I am extremely pleased
to share with the reader some of the details of this four-month visit. To begin
with, I would like to say a few words about the group itself.
EnyAC is a notable research group whose work is always anticipated and
highly appreciated by the respective community. Its strength is in the talented
individuals and in the adamant leadership. Both are merits of Professor Diana
Marculescu, who is the group’s leader. EnyAC develops tools, frameworks, and
methodologies for sustainable computing, computing for sustainability, and
life-science applications. Since its inception in 2000, the group has worked on
novel computing paradigms and computer-aided design tools for energy-, variability-, and reliability-aware computing, all aiming at sustainable computing.
In what follow, I would like to explain what drove me to undertake this transcontinental journey and to highlight the major checkpoints of this undertaking.
Motivation
My desire to visit EnyAC was due to several factors. First of all, EnyAC’s research is tightly related to my PhD studies in the Embedded Systems Laboratory
(ESLAB) at Linköping University (LiU). Therefore, I became familiar with their
work fairly early on, and I inevitably got to admire it. The trip was then a great
opportunity for me to meet in person the people that I looked up to. Second, the
visit would allow me to develop further the research ideas that I had at that time
and to explore new ones and incorporate them into my work. Third, the visit
manifested itself as, and it undoubtedly was, a source of valuable experience for
a researcher since one would get a change to collaborate closely with people in
a completely different research environment; after all, it was a different university and in a different country with all cultural and social implications included.
Needless to mention that CMU is one of the most prestigious universities in the
US, and this collaboration was also important to ESLAB, to the Department of
Computer and Information Science, and to LiU in general.
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Agenda
The topic of my research is uncertainty quantification for electronic-system design. The core problem that this line of research is concerned with is the uncertainty faced by the designer of electronic systems. In many cases, this uncertainty
is inherent, inevitable. One of the reasons for this state of affairs is process variation: the properties of fabricated dies deviate from the nominal ones since process parameters cannot be controlled precisely using the technologies currently
employed in the fabrication process. Another reason is aging: the performance
of an electronic system degrades over time due to natural or accelerated wear. Yet
another, and perhaps the most consequential, reason is workload variation: the
demand on a system in the field is rarely, if ever, known in the lab. Such uncertainties have to be properly dealt with in order to achieve effectiveness, efficiency,
and robustness, and this is the theme that penetrates my research.
Prior to my visit to EnyAC, the techniques that we were developing at ESLAB
together with my advisors Professor Zebo Peng and Professor Petru Eles were
meant to be used in off-chip settings, that is, offline, at design time. The agenda
for the trip was to consider on-chip settings as well since being on the chip opens
up additional means of dealing with the aforementioned uncertainty. The attractiveness of this route is due to the fact that only one particular fabricated hardware in one particular environment needs to be considered. More importantly,
being on the chip enables adaptation. This means that instances of the system are
treated on a case-by-case basis: each instance attains a custom-built, fine-tuned
solution, and this solution evolves over time, diligently following any changes.
Research
During the first month of my visit, I was primarily preoccupied with reading the
literature on dynamic management 0f electronic systems. I paid particular attention to proactive management since it is considered to be the most efficient one.
A proactive management strategy tries to predict the future state of the system
under consideration and act in accordance with this prediction. Such strategies
are largely about making a good use of on-chip data as these data capture what
the system is actually going through, which is invaluable for forecasting.
Nowadays, “making a good use of … data” is firmly, and not without a strong
reason, associated with the fields of statistics and machine learning. These fields
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provide the tools for learning from data and drawing well-grounded conclusions
with respect to the generative process underlying these data. These tools are at
heart of proactive management strategies. It should be clear, however, that having data at one’s disposal is an unconditional prerequisite to any of such tools.
My foremost task was then to set up a research environment for myself that
would allow me to explore and experiment with data-driven techniques. This
primarily means that I needed a stable supply of the data that a potential management strategy would leverage in order to attain its management objectives.
In such cases, computer simulators are of great help since real hardware often is
unavailable (for technical, financial, or otherwise reasons), is not an appropriate
place for early experimentation, or gets in the way of fluid exploration.
In order to be useful for learning purposes, simulations should be sufficiently
detailed so that they capture well the characteristics of real systems. Although
detailed simulations are practical for the design of individual components, such
simulations fall short when it comes to complex systems. A modern electronic
system is reasonably complex, and it might take days for a state-of-the-art simulator to simulate a short, in wall-clock time, program running on such a system.
This scheme is not affordable for designing learning-based management strategies as learning requires many simulations with potentially large payloads.
All in all, the situation was daunting for a researcher trying to leverage the rich
machinery of statistics and machine learning. The need for alternative sources of
high-quality data fuel was prominent. More broadly, there was a prominent need
for a systematic assistance in obtaining data for learning-based research studies,
which was concluded based on the literature and on my personal experience.
Providing such an assistance became a research work in its own right, which
occupied the rest of my stay in Pittsburgh, as I will describe next.
My advisors—Professor Zebo Peng and Professor Petru Eles at LiU and Professor Diana Marculescu at CMU—and I decided to take time and develop a
methodology for fast generation of on-chip data with data-powered applications
in mind, like those that we originally had. More concretely, we set out to construct a fast technique for producing power and temperature data of electronic
systems, which would capture well the idiosyncrasies of real data and, hence,
would be suitable for devising learning-based management strategies.
The reason for focusing on power and temperature is due to the fact that power consumption and heat dissipation are of immense importance for the well-being of electronic systems. Power is tightly related to energy, and energy translates
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willingly to hours of battery life and to electricity bills. Operating temperature, on
the other hand, is one of the major causes of permanent damage, which necessitates the deployment of adequate cooling equipments, escalating overall product
expenses. Under these circumstances, power and temperature are arguably the
main parameters to consider when managing electronic systems.
Developing a methodology that would satisfy the requirements that had been
imposed—a high generation speed and a high level of realism were needed—was
not straightforward. A lot of aspects pertaining to the functioning of electronic systems and the interactions of such systems with the corresponding environments had to be carefully thought through. Nevertheless, by the end of the
fourth month, all the components of our methodology were in place. Moreover,
we had implemented a prototype of a toolchain embodying the methodology.
At the time I am writing these lines, I am obviously already back in Linköping,
and we are planning to publish the methodology that we have developed and
to polish and open-source the accompanying toolchain. We hope that our work
will enable new and assist ongoing studies by making it easier to explore the potential of novel or revived data-driven techniques for analysis, prediction, and
management of electronic systems.
Conclusion
The visit to the EnyAC research group was a highly valuable, productive experience from both personal and professional standpoints. I can confidently say
that my expectations about the group, the university, and the country were well
exceeded. I met many remarkable people, whom I enjoyed working with, and I
will try to do my best to keep in touch with them.
Acknowledgments
I would like to thank the faculty and staff of the Department of Computer and
Electrical Engineering at CMU for their warm hospitality. I would like to express
my particular gratitude to Diana Marculescu, Zhuo Chen, and Ermao Cai for the
exhaustive help with everything that I could possibly need. I also would like to
thank Ina Fiterau, who is with the School of Computer Science at CMU, for the
pieces of advice regarding machine learning. Lastly, I would like to acknowledge
Swedish National Graduate School in Computer Science for funding this visit.
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