10,309 research outputs found
Heterogeneous Secure Multi-level Remote Acceleration Service for Low-Power Integrated Systems and Devices
AbstractThis position paper presents a novel heterogeneous CPU-GPU multi-level cloud acceleration focusing on applications running on embedded systems found on low-power devices. A runtime system performs energy and performance estimations in order to automatically select local CPU-based and GPU-based tasks that should be seamlessly executed on more powerful remote devices or cloud infrastructures. Moreover, it proposes, for the first time, a secure unified model where almost any device or infrastructure can operate as an accelerated entity and/or as an accelerator serving other less powerful devices in a secure way
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Multi-Tenant Cloud FPGA: A Survey on Security
With the exponentially increasing demand for performance and scalability in
cloud applications and systems, data center architectures evolved to integrate
heterogeneous computing fabrics that leverage CPUs, GPUs, and FPGAs. FPGAs
differ from traditional processing platforms such as CPUs and GPUs in that they
are reconfigurable at run-time, providing increased and customized performance,
flexibility, and acceleration. FPGAs can perform large-scale search
optimization, acceleration, and signal processing tasks compared with power,
latency, and processing speed. Many public cloud provider giants, including
Amazon, Huawei, Microsoft, Alibaba, etc., have already started integrating
FPGA-based cloud acceleration services. While FPGAs in cloud applications
enable customized acceleration with low power consumption, it also incurs new
security challenges that still need to be reviewed. Allowing cloud users to
reconfigure the hardware design after deployment could open the backdoors for
malicious attackers, potentially putting the cloud platform at risk.
Considering security risks, public cloud providers still don't offer
multi-tenant FPGA services. This paper analyzes the security concerns of
multi-tenant cloud FPGAs, gives a thorough description of the security problems
associated with them, and discusses upcoming future challenges in this field of
study
Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges
Heterogeneous Vehicular NETworks (HetVNETs) can meet various
quality-of-service (QoS) requirements for intelligent transport system (ITS)
services by integrating different access networks coherently. However, the
current network architecture for HetVNET cannot efficiently deal with the
increasing demands of rapidly changing network landscape. Thanks to the
centralization and flexibility of the cloud radio access network (Cloud-RAN),
soft-defined networking (SDN) can conveniently be applied to support the
dynamic nature of future HetVNET functions and various applications while
reducing the operating costs. In this paper, we first propose the multi-layer
Cloud RAN architecture for implementing the new network, where the multi-domain
resources can be exploited as needed for vehicle users. Then, the high-level
design of soft-defined HetVNET is presented in detail. Finally, we briefly
discuss key challenges and solutions for this new network, corroborating its
feasibility in the emerging fifth-generation (5G) era
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