System identification: theory for the user
System identification: theory for the user
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Digital image processing
Neural networks and the bias/variance dilemma
Neural Computation
Testability of artificial neural networks: a behavioral approach
Journal of Electronic Testing: Theory and Applications
Mantissa-Preserving Operations and Robust Algorithm-Based Fault Tolerance for Matrix Computations
IEEE Transactions on Computers
A Novel Implementation of CORDIC Algorithm Using Backward Angle Recoding (BAR)
IEEE Transactions on Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification Using Adaptive Wavelets for Feature Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accuracy vs. Precision in Digital VLSI Architectures for Signal Processing
IEEE Transactions on Computers
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Finite Precision Error Analysis of Neural Network Hardware Implementations
IEEE Transactions on Computers
A Fast and Robust Network Bisection Algorithm
IEEE Transactions on Computers
IEEE Transactions on Computers
Embedded System Design Issues (The Rest of the Story)
ICCD '96 Proceedings of the 1996 International Conference on Computer Design, VLSI in Computers and Processors
High Speed DCT/IDCT Using a Pipelined CORDIC Algorithm
ARITH '95 Proceedings of the 12th Symposium on Computer Arithmetic
Learning and Generalization: With Applications to Neural Networks
Learning and Generalization: With Applications to Neural Networks
The selection of weight accuracies for Madalines
IEEE Transactions on Neural Networks
The effects of quantization on multilayer neural networks
IEEE Transactions on Neural Networks
Word-length optimization for differentiable nonlinear systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation
Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation
Survey paper: Research on probabilistic methods for control system design
Automatica (Journal of IFAC)
| Hi-index | 14.98 |
This paper provides a methodology for analyzing the performance degradation of a computation once affected by perturbations. The suggested methodology, by relaxing all assumptions made in the related literature, provides design guidelines for the subsequent implementation of complex computations in physical devices. Implementation issues, such as finite precision representation, fluctuations of the production parameters, and aging effects, can be studied directly at system level, independently from any technological aspect and quantization technique. Only the behavioral description of the computational flow, which is assumed to be Lebesgue measurable and the architecture to be investigated are needed. The suggested analysis is based on the recent theory of Randomized Algorithms, which transforms the computationally intractable problem of robustness investigation in a poly-time algorithm by resorting to probability.