A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Digital image processing
A VHDL-based design methodology: the design experience of a high performance ASIC chip
EURO-DAC '94 Proceedings of the conference on European design automation
Finite Precision Error Analysis of Neural Network Hardware Implementations
IEEE Transactions on Computers
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
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
Randomized Algorithms: A System-Level, Poly-Time Analysis of Robust Computation
IEEE Transactions on Computers
Robust low-sensitivity Adaline neuron based on Continuous Valued Number System
Analog Integrated Circuits and Signal Processing
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The paper provides a sensitivity analysis to measure the loss in accuracy induced by perturbations affecting acyclic computational flows composed of linear convolutions and nonlinear functions. We do not assume a large number of coefficients or input independence for the convolution module, nor strict requirements on the nonlinear function. The analysis is tailored to digital VLSI implementations where perturbations, associated with data quantization, affect the device inputs, coefficients, internal values, and outputs. The sensitivity analysis can be used to measure the loss in accuracy along the computational chain, to characterize the tolerated perturbations, and to dimension the whole architecture.