749 research outputs found
Autonomous Recovery Of Reconfigurable Logic Devices Using Priority Escalation Of Slack
Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases. To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Recon- figurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric. FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A iii significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria
A proposed synthesis method for Application-Specific Instruction Set Processors
Due to the rapid technology advancement in integrated circuit era, the need for the high computation
performance together with increasing complexity and manufacturing costs has raised the demand for
high-performance con
fi
gurable designs; therefore, the Application-Speci
fi
c Instruction Set Processors
(ASIPs) are widely used in SoC design. The automated generation of software tools for ASIPs is a
commonly used technique, but the automated hardware model generation is less frequently applied in
terms of
fi
nal RTL implementations. Contrary to this, the
fi
nal register-transfer level models are usually
created, at least partly, manually. This paper presents a novel approach for automated hardware model
generation for ASIPs. The new solution is based on a novel abstract ASIP model and a modeling language
(Algorithmic Microarchitecture Description Language, AMDL) optimized for this architecture model. The
proposed AMDL-based pre-synthesis method is based on a set of pre-de
fi
ned VHDL implementation
schemes, which ensure the qualities of the automatically generated register-transfer level models in
terms of resource requirement and operation frequency. The design framework implementing the
algorithms required by the synthesis method is also presented
A configurable vector processor for accelerating speech coding algorithms
The growing demand for voice-over-packer (VoIP) services and multimedia-rich
applications has made increasingly important the efficient, real-time implementation of
low-bit rates speech coders on embedded VLSI platforms. Such speech coders are
designed to substantially reduce the bandwidth requirements thus enabling dense multichannel
gateways in small form factor. This however comes at a high computational cost
which mandates the use of very high performance embedded processors.
This thesis investigates the potential acceleration of two major ITU-T speech coding
algorithms, namely G.729A and G.723.1, through their efficient implementation on a
configurable extensible vector embedded CPU architecture. New scalar and vector ISAs
were introduced which resulted in up to 80% reduction in the dynamic instruction count
of both workloads. These instructions were subsequently encapsulated into a parametric,
hybrid SISD (scalar processor)âSIMD (vector) processor. This work presents the research
and implementation of the vector datapath of this vector coprocessor which is tightly-coupled
to a Sparc-V8 compliant CPU, the optimization and simulation methodologies
employed and the use of Electronic System Level (ESL) techniques to rapidly design
SIMD datapaths
Harnessing resilience: biased voltage overscaling for probabilistic signal processing
A central component of modern computing is the idea that computation requires
determinism. Contrary to this belief, the primary contribution of this work shows that
useful computation can be accomplished in an error-prone fashion. Focusing on low-power
computing and the increasing push toward energy conservation, the work seeks to sacrifice
accuracy in exchange for energy savings.
Probabilistic computing forms the basis for this error-prone computation by diverging from the requirement of determinism and allowing for randomness within computing.
Implemented as probabilistic CMOS (PCMOS), the approach realizes enormous energy sav-
ings in applications that require probability at an algorithmic level. Extending probabilistic
computing to applications that are inherently deterministic, the biased voltage overscaling
(BIVOS) technique presented here constrains the randomness introduced through PCMOS.
Doing so, BIVOS is able to limit the magnitude of any resulting deviations and realizes
energy savings with minimal impact to application quality.
Implemented for a ripple-carry adder, array multiplier, and finite-impulse-response (FIR)
filter; a BIVOS solution substantially reduces energy consumption and does so with im-
proved error rates compared to an energy equivalent reduced-precision solution. When
applied to H.264 video decoding, a BIVOS solution is able to achieve a 33.9% reduction in
energy consumption while maintaining a peak-signal-to-noise ratio of 35.0dB (compared to
14.3dB for a comparable reduced-precision solution).
While the work presented here focuses on a specific technology, the technique realized
through BIVOS has far broader implications. It is the departure from the conventional
mindset that useful computation requires determinism that represents the primary innovation of this work. With applicability to emerging and yet to be discovered technologies,
BIVOS has the potential to contribute to computing in a variety of fashions.PhDCommittee Chair: Anderson, David; Committee Member: Conte, Thomas; Committee Member: Ferri, Bonnie; Committee Member: Hasler, Paul; Committee Member: Mooney, Vincen
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