362 research outputs found
Smart Chips for Smart Surroundings -- 4S
The overall mission of the 4S project (Smart Chips for Smart Surroundings) was to define and develop efficient flexible, reconfigurable core building blocks, including the supporting tools, for future Ambient System Devices. Reconfigurability offers the needed flexibility and adaptability, it provides the efficiency needed for these systems, it enables systems that can adapt to rapidly changing environmental conditions, it enables communication over heterogeneous wireless networks, and it reduces risks: reconfigurable systems can adapt to standards that may vary from place to place or standards that have changed during and after product development. In 4S we focused on heterogeneous building blocks such as analogue, hardwired functions, fine and coarse grain reconfigurable tiles and microprocessors. Such a platform can adapt to a wide application space without the need for specialized ASICs. A novel power aware design flow and runtime system was developed. The runtime system decides dynamically about the near-optimal application mapping to the given hardware platform. The overall concept was verified on hardware platforms based on an existing SoC and in a second step with novel silicon. DRM (Digital Radio Mondiale) and MPEG4 Video applications have been implemented on the platforms demonstrating the adaptability of the 4S concept
Darwinian Data Structure Selection
Data structure selection and tuning is laborious but can vastly improve an
application's performance and memory footprint. Some data structures share a
common interface and enjoy multiple implementations. We call them Darwinian
Data Structures (DDS), since we can subject their implementations to survival
of the fittest. We introduce ARTEMIS a multi-objective, cloud-based
search-based optimisation framework that automatically finds optimal, tuned DDS
modulo a test suite, then changes an application to use that DDS. ARTEMIS
achieves substantial performance improvements for \emph{every} project in
Java projects from DaCapo benchmark, popular projects and uniformly
sampled projects from GitHub. For execution time, CPU usage, and memory
consumption, ARTEMIS finds at least one solution that improves \emph{all}
measures for () of the projects. The median improvement across
the best solutions is , , for runtime, memory and CPU
usage.
These aggregate results understate ARTEMIS's potential impact. Some of the
benchmarks it improves are libraries or utility functions. Two examples are
gson, a ubiquitous Java serialization framework, and xalan, Apache's XML
transformation tool. ARTEMIS improves gson by \%, and for
memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by
\%. \emph{Every} client of these projects will benefit from these
performance improvements.Comment: 11 page
A VHDL-AMS Simulation Environment for an UWB Impulse Radio Transceiver
Ultra-Wide-Band (UWB) communication based on the impulse radio paradigm is becoming increasingly popular. According to the IEEE 802.15 WPAN Low Rate Alternative PHY Task Group 4a, UWB will play a major role in localization applications, due to the high time resolution of UWB signals which allow accurate indirect measurements of distance between transceivers. Key for the successful implementation of UWB transceivers is the level of integration that will be reached, for which a simulation environment that helps take appropriate design decisions is crucial. Owing to this motivation, in this paper we propose a multiresolution UWB simulation environment based on the VHDL-AMS hardware description language, along with a proper methodology which helps tackle the complexity of designing a mixed-signal UWB System-on-Chip. We applied the methodology and used the simulation environment for the specification and design of an UWB transceiver based on the energy detection principle. As a by-product, simulation results show the effectiveness of UWB in the so-called ranging application, that is the accurate evaluation of the distance between a couple of transceivers using the two-way-ranging metho
SecDDR: Enabling Low-Cost Secure Memories by Protecting the DDR Interface
The security goals of cloud providers and users include memory
confidentiality and integrity, which requires implementing Replay-Attack
protection (RAP). RAP can be achieved using integrity trees or mutually
authenticated channels. Integrity trees incur significant performance overheads
and are impractical for protecting large memories. Mutually authenticated
channels have been proposed only for packetized memory interfaces that address
only a very small niche domain and require fundamental changes to memory system
architecture. We propose SecDDR, a low-cost RAP that targets direct-attached
memories, like DDRx. SecDDR avoids memory-side data authentication, and thus,
only adds a small amount of logic to memory components and does not change the
underlying DDR protocol, making it practical for widespread adoption. In
contrast to prior mutual authentication proposals, which require trusting the
entire memory module, SecDDR targets untrusted modules by placing its limited
security logic on the DRAM die (or package) of the ECC chip. Our evaluation
shows that SecDDR performs within 1% of an encryption-only memory without RAP
and that SecDDR provides 18.8% and 7.8% average performance improvements (up to
190.4% and 24.8%) relative to a 64-ary integrity tree and an authenticated
channel, respectively
A Praise for Defensive Programming: Leveraging Uncertainty for Effective Malware Mitigation
A promising avenue for improving the effectiveness of behavioral-based
malware detectors would be to combine fast traditional machine learning
detectors with high-accuracy, but time-consuming deep learning models. The main
idea would be to place software receiving borderline classifications by
traditional machine learning methods in an environment where uncertainty is
added, while software is analyzed by more time-consuming deep learning models.
The goal of uncertainty would be to rate-limit actions of potential malware
during the time consuming deep analysis. In this paper, we present a detailed
description of the analysis and implementation of CHAMELEON, a framework for
realizing this uncertain environment for Linux. CHAMELEON offers two
environments for software: (i) standard - for any software identified as benign
by conventional machine learning methods and (ii) uncertain - for software
receiving borderline classifications when analyzed by these conventional
machine learning methods. The uncertain environment adds obstacles to software
execution through random perturbations applied probabilistically on selected
system calls. We evaluated CHAMELEON with 113 applications and 100 malware
samples for Linux. Our results showed that at threshold 10%, intrusive and
non-intrusive strategies caused approximately 65% of malware to fail
accomplishing their tasks, while approximately 30% of the analyzed benign
software to meet with various levels of disruption. With a dynamic, per-system
call threshold, CHAMELEON caused 92% of the malware to fail, and only 10% of
the benign software to be disrupted. We also found that I/O-bound software was
three times more affected by uncertainty than CPU-bound software. Further, we
analyzed the logs of software crashed with non-intrusive strategies, and found
that some crashes are due to the software bugs
Interim research assessment 2003-2005 - Computer Science
This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities
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