60,133 research outputs found
Do DL models and training environments have an impact on energy consumption?
Current research in the computer vision field mainly focuses on improving
Deep Learning (DL) correctness and inference time performance. However, there
is still little work on the huge carbon footprint that has training DL models.
This study aims to analyze the impact of the model architecture and training
environment when training greener computer vision models. We divide this goal
into two research questions. First, we analyze the effects of model
architecture on achieving greener models while keeping correctness at optimal
levels. Second, we study the influence of the training environment on producing
greener models. To investigate these relationships, we collect multiple metrics
related to energy efficiency and model correctness during the models' training.
Then, we outline the trade-offs between the measured energy efficiency and the
models' correctness regarding model architecture, and their relationship with
the training environment. We conduct this research in the context of a computer
vision system for image classification. In conclusion, we show that selecting
the proper model architecture and training environment can reduce energy
consumption dramatically (up to 98.83%) at the cost of negligible decreases in
correctness. Also, we find evidence that GPUs should scale with the models'
computational complexity for better energy efficiency.Comment: 49th Euromicro Conference Series on Software Engineering and Advanced
Applications (SEAA). 8 pages, 3 figure
POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
Most of computer science focuses on automatically solving given computational
problems. I focus on automatically inventing or discovering problems in a way
inspired by the playful behavior of animals and humans, to train a more and
more general problem solver from scratch in an unsupervised fashion. Consider
the infinite set of all computable descriptions of tasks with possibly
computable solutions. The novel algorithmic framework POWERPLAY (2011)
continually searches the space of possible pairs of new tasks and modifications
of the current problem solver, until it finds a more powerful problem solver
that provably solves all previously learned tasks plus the new one, while the
unmodified predecessor does not. Wow-effects are achieved by continually making
previously learned skills more efficient such that they require less time and
space. New skills may (partially) re-use previously learned skills. POWERPLAY's
search orders candidate pairs of tasks and solver modifications by their
conditional computational (time & space) complexity, given the stored
experience so far. The new task and its corresponding task-solving skill are
those first found and validated. The computational costs of validating new
tasks need not grow with task repertoire size. POWERPLAY's ongoing search for
novelty keeps breaking the generalization abilities of its present solver. This
is related to Goedel's sequence of increasingly powerful formal theories based
on adding formerly unprovable statements to the axioms without affecting
previously provable theorems. The continually increasing repertoire of problem
solving procedures can be exploited by a parallel search for solutions to
additional externally posed tasks. POWERPLAY may be viewed as a greedy but
practical implementation of basic principles of creativity. A first
experimental analysis can be found in separate papers [53,54].Comment: 21 pages, additional connections to previous work, references to
first experiments with POWERPLA
ANCHOR: logically-centralized security for Software-Defined Networks
While the centralization of SDN brought advantages such as a faster pace of
innovation, it also disrupted some of the natural defenses of traditional
architectures against different threats. The literature on SDN has mostly been
concerned with the functional side, despite some specific works concerning
non-functional properties like 'security' or 'dependability'. Though addressing
the latter in an ad-hoc, piecemeal way, may work, it will most likely lead to
efficiency and effectiveness problems. We claim that the enforcement of
non-functional properties as a pillar of SDN robustness calls for a systemic
approach. As a general concept, we propose ANCHOR, a subsystem architecture
that promotes the logical centralization of non-functional properties. To show
the effectiveness of the concept, we focus on 'security' in this paper: we
identify the current security gaps in SDNs and we populate the architecture
middleware with the appropriate security mechanisms, in a global and consistent
manner. Essential security mechanisms provided by anchor include reliable
entropy and resilient pseudo-random generators, and protocols for secure
registration and association of SDN devices. We claim and justify in the paper
that centralizing such mechanisms is key for their effectiveness, by allowing
us to: define and enforce global policies for those properties; reduce the
complexity of controllers and forwarding devices; ensure higher levels of
robustness for critical services; foster interoperability of the non-functional
property enforcement mechanisms; and promote the security and resilience of the
architecture itself. We discuss design and implementation aspects, and we prove
and evaluate our algorithms and mechanisms, including the formalisation of the
main protocols and the verification of their core security properties using the
Tamarin prover.Comment: 42 pages, 4 figures, 3 tables, 5 algorithms, 139 reference
Abstract State Machines 1988-1998: Commented ASM Bibliography
An annotated bibliography of papers which deal with or use Abstract State
Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm
First steps towards the certification of an ARM simulator using Compcert
The simulation of Systems-on-Chip (SoC) is nowadays a hot topic because,
beyond providing many debugging facilities, it allows the development of
dedicated software before the hardware is available. Low-consumption CPUs such
as ARM play a central role in SoC. However, the effectiveness of simulation
depends on the faithfulness of the simulator. To this effect, we propose here
to prove significant parts of such a simulator, SimSoC. Basically, on one hand,
we develop a Coq formal model of the ARM architecture while on the other hand,
we consider a version of the simulator including components written in
Compcert-C. Then we prove that the simulation of ARM operations, according to
Compcert-C formal semantics, conforms to the expected formal model of ARM. Size
issues are partly dealt with using automatic generation of significant parts of
the Coq model and of SimSoC from the official textual definition of ARM.
However, this is still a long-term project. We report here the current stage of
our efforts and discuss in particular the use of Compcert-C in this framework.Comment: First International Conference on Certified Programs and Proofs 7086
(2011
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