A
Ali Ahmadi
Researcher at University of Texas at Dallas
Publications - 23
Citations - 610
Ali Ahmadi is an academic researcher from University of Texas at Dallas. The author has contributed to research in topics: Cost reduction & Fault tolerance. The author has an hindex of 8, co-authored 23 publications receiving 470 citations. Previous affiliations of Ali Ahmadi include University of Tehran & University of Kashan.
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Journal ArticleDOI
Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications
TL;DR: It is shown that these proposed Bio-inspired Imprecise Computational blocks (BICs) can be exploited to efficiently implement a three-layer face recognition neural network and the hardware defuzzification block of a fuzzy processor.
Proceedings ArticleDOI
A Low-Cost Fault-Tolerant Approach for Hardware Implementation of Artificial Neural Networks
TL;DR: A new method for fault-tolerant implementation of neural networks that detects and corrects any single fault in the network and achieves complete fault tolerance for single faults with at most 40% area overhead.
Proceedings ArticleDOI
Brain-Computer Interface Signal Processing Algorithms: A Computational Cost vs. Accuracy Analysis for Wearable Computers
TL;DR: A computational profiling on signal processing tasks for a typical BCI system is performed and adaptive algorithms that will adjust the computational complexity of the signal processing based on the amount of energy available are investigated, while guaranteeing that the accuracy is minimally compromised.
Proceedings ArticleDOI
Wafer-level process variation-driven probe-test flow selection for test cost reduction in analog/RF ICs
TL;DR: This work introduces a methodology for dynamically selecting whether to subject a wafer to a complete or a reduced probe-test flow, while ensuring that the concomitant test cost savings do not compromise test quality.
Proceedings ArticleDOI
Workload characterization and prediction: A pathway to reliable multi-core systems
TL;DR: This work discusses the state-of-the-art methods for predicting workload dynamics and compares their performance, and introduces a prediction method based on Support Vector Regression (SVR), which accurately predicts the workload behavior several steps ahead.