10,534 research outputs found
Asynchronous Circuit Stacking for Simplified Power Management
As digital integrated circuits (ICs) continue to increase in complexity, new challenges arise for designers. Complex ICs are often designed by incorporating multiple power domains therefore requiring multiple voltage converters to produce the corresponding supply voltages. These converters not only take substantial on-chip layout area and/or off-chip space, but also aggregate the power loss during the voltage conversions that must occur fast enough to maintain the necessary power supplies. This dissertation work presents an asynchronous Multi-Threshold NULL Convention Logic (MTNCL) “stacked” circuit architecture that alleviates this problem by reducing the number of voltage converters needed to supply the voltage the ICs operate at. By stacking multiple MTNCL circuits between power and ground, supplying a multiple of VDD to the entire stack and incorporating simple control mechanisms, the dynamic range fluctuation problem can be mitigated. A 130nm Bulk CMOS process and a 32nm Silicon-on-Insulator (SOI) CMOS process are used to evaluate the theoretical effect of stacking different circuitry while running different workloads. Post parasitic physical implementations are then carried out in the 32nm SOI process for demonstrating the feasibility and analyzing the advantages of the proposed MTNCL stacking architecture
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A RISC-V Vector Processor With Simultaneous-Switching Switched-Capacitor DC-DC Converters in 28 nm FDSOI
This work demonstrates a RISC-V vector microprocessor implemented in 28 nm FDSOI with fully integrated simultaneous-switching switched-capacitor DC-DC (SC DC-DC) converters and adaptive clocking that generates four on-chip voltages between 0.45 and 1 V using only 1.0 V core and 1.8 V IO voltage inputs. The converters achieve high efficiency at the system level by switching simultaneously to avoid charge-sharing losses and by using an adaptive clock to maximize performance for the resulting voltage ripple. Details about the implementation of the DC-DC switches, DC-DC controller, and adaptive clock are provided, and the sources of conversion loss are analyzed based on measured results. This system pushes the capabilities of dynamic voltage scaling by enabling fast transitions (20 ns), simple packaging (no off-chip passives), low area overhead (16%), high conversion efficiency (80%-86%), and high energy efficiency (26.2 DP GFLOPS/W) for mobile devices
Arcing High Impedance Fault Detection Using Real Coded Genetic Algorithm
Safety and reliability are two of the most important aspects of electric power supply systems. Sensitivity and robustness to detect and isolate faults can influence the safety and reliability of such systems. Overcurrent relays are generally used to protect the high voltage feeders in distribution systems. Downed conductors, tree branches touching conductors, and failing insulators often cause high-impedance faults in overhead distribution systems. The levels of currents of these faults are often much smaller than detection thresholds of traditional ground fault detection devices, thus reliable detection of these high impedance faults is a real challenge. With modern signal processing techniques, special hardware and software can be used to significantly improve the reliability of detection of certain types of faults. This paper presents a new method for detecting High Impedance Faults (HIF) in distribution systems using real coded genetic algorithm (RCGA) to analyse the harmonics and phase angles of the fault current signals. The method is used to discriminate HIFs by identifying specific events that happen when a HIF occurs
Intelligent Fault Analysis in Electrical Power Grids
Power grids are one of the most important components of infrastructure in
today's world. Every nation is dependent on the security and stability of its
own power grid to provide electricity to the households and industries. A
malfunction of even a small part of a power grid can cause loss of
productivity, revenue and in some cases even life. Thus, it is imperative to
design a system which can detect the health of the power grid and take
protective measures accordingly even before a serious anomaly takes place. To
achieve this objective, we have set out to create an artificially intelligent
system which can analyze the grid information at any given time and determine
the health of the grid through the usage of sophisticated formal models and
novel machine learning techniques like recurrent neural networks. Our system
simulates grid conditions including stimuli like faults, generator output
fluctuations, load fluctuations using Siemens PSS/E software and this data is
trained using various classifiers like SVM, LSTM and subsequently tested. The
results are excellent with our methods giving very high accuracy for the data.
This model can easily be scaled to handle larger and more complex grid
architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure
PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network
The ubiquitous presence of printed circuit boards (PCBs) in modern electronic
systems and embedded devices makes their integrity a top security concern. To
take advantage of the economies of scale, today's PCB design and manufacturing
are often performed by suppliers around the globe, exposing them to many
security vulnerabilities along the segmented PCB supply chain. Moreover, the
increasing complexity of the PCB designs also leaves ample room for numerous
sneaky board-level attacks to be implemented throughout each stage of a PCB's
lifetime, threatening many electronic devices. In this paper, we propose
PDNPulse, a power delivery network (PDN) based PCB anomaly detection framework
that can identify a wide spectrum of board-level malicious modifications.
PDNPulse leverages the fact that the PDN's characteristics are inevitably
affected by modifications to the PCB, no matter how minuscule. By detecting
changes to the PDN impedance profile and using the Frechet distance-based
anomaly detection algorithms, PDNPulse can robustly and successfully discern
malicious modifications across the system. Using PDNPulse, we conduct extensive
experiments on seven commercial-off-the-shelf PCBs, covering different design
scales, different threat models, and seven different anomaly types. The results
confirm that PDNPulse creates an effective security asymmetry between attack
and defense
Via transition modeling and charge replenishment of the power delivery network in multilayer PCBs
In the first article of this thesis, the charge delivery in the power distribution network for printed circuit board has been analyzed in the time-domain. Performing all the simulations and analyzing the PDN physics and modeling, I contributed to a better understanding of the time-domain decoupling mechanism. The second paper studies the noise coupling sing a segmentation approach combined with a via-to-antipad capacitance model and a plane-pair cavity model. Building equivalent circuit models as well as analyzing design strategies, I contributed to a new approach for the PDN analysis in multilayer PCBs. The third article discusses how to estimate the amount of current needed for large ICs and how to evaluate the amount of noise voltage due to this current draw. After accurate discussion of the design strategies, I modeled and simulated the free evolution of a charged PCB with and without decoupling capacitors. The depletion of charges stored between the power buses in time and frequency-domain has been investigated as a function of the plane thickness, SMT decoupling closeness in the fourth paper. With my contribution, the time and frequency-domain in the PDN have been related using circuit approach. In the fifth paper, I analyzed a 26-layer printed circuit board performing milling, measurements and building circuit models. It is the first time that the segmentation approach has been used for differential geometry. In addition, Debye materials have been implemented in the cavity model --Abstract, page iv
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