F
Fabrizio Lombardi
Researcher at Northeastern University
Publications - 677
Citations - 12743
Fabrizio Lombardi is an academic researcher from Northeastern University. The author has contributed to research in topics: Fault detection and isolation & Redundancy (engineering). The author has an hindex of 51, co-authored 639 publications receiving 10357 citations. Previous affiliations of Fabrizio Lombardi include Helsinki University of Technology & Fudan University.
Papers
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A Modified Partial Product Generator for Redundant Binary Multipliers
TL;DR: Simulation results show that the proposed RBMPPG based designs significantly improve the area and power consumption when the word length of each operand in the multiplier is at least 32 bits; these reductions over previous NB multiplier designs incur in a modest delay increase.
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A High-Performance and Energy-Efficient FIR Adaptive Filter Using Approximate Distributed Arithmetic Circuits
TL;DR: The radix-8 Booth algorithm is used to reduce the number of partial products in the DA architecture, although no multiplication is explicitly performed, and the proposed design achieves 45%–61% lower EPO compared with the DLMS design.
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Design and process variation analysis of CNTFET-based ternary memory cells
Geunho Cho,Fabrizio Lombardi +1 more
TL;DR: Two novel ternary CNTFET-based SRAM cells are proposed in this paper; in nearly all cases, the proposed cells outperform existing CNTFets by showing a small standard deviation in the simulated memory circuits.
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A Stochastic Approach for the Analysis of Dynamic Fault Trees With Spare Gates Under Probabilistic Common Cause Failures
TL;DR: Stochastic computational models are proposed for an efficient analysis of spare gates and PCCFs in a DFT using a non-Bernoulli sequence of random permutations of fixed numbers of ones and zeros.
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Analysis of missing and additional cell defects in sequential quantum-dot cellular automata
TL;DR: It is shown that a device-level characterization of the defects and faults can be consistently extended to a circuit-level analysis.