M
Mehdi Kamal
Researcher at University of Tehran
Publications - 101
Citations - 1570
Mehdi Kamal is an academic researcher from University of Tehran. The author has contributed to research in topics: Adder & Energy consumption. The author has an hindex of 17, co-authored 90 publications receiving 944 citations. Previous affiliations of Mehdi Kamal include Sharif University of Technology.
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Dual-Quality 4:2 Compressors for Utilizing in Dynamic Accuracy Configurable Multipliers
TL;DR: Four 4:2 compressors, which have the flexibility of switching between the exact and approximate operating modes, are proposed, which are used in the structures of parallel multipliers provides configurable multipliers whose accuracies may change dynamically during the runtime.
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RAP-CLA: A Reconfigurable Approximate Carry Look-Ahead Adder
TL;DR: A fast yet energy-efficient reconfigurable approximate carry look-ahead adder that has the ability of switching between the approximate and exact operating modes making it suitable for both error-resilient and exact applications is proposed.
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RoBA Multiplier: A Rounding-Based Approximate Multiplier for High-Speed yet Energy-Efficient Digital Signal Processing
TL;DR: An approximate multiplier that is high speed yet energy efficient is proposed that is to round the operands to the nearest exponent of two improving speed and energy consumption at the price of a small error.
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TOSAM: An Energy-Efficient Truncation- and Rounding-Based Scalable Approximate Multiplier
TL;DR: The proposed approximate multiplier has an almost Gaussian error distribution with a near-zero mean value and is exploited in the structure of a JPEG encoder, sharpening, and classification applications, indicating that the quality degradation of the output is negligible.
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Block-Based Carry Speculative Approximate Adder for Energy-Efficient Applications
TL;DR: The effectiveness of the proposed approximate adder is compared with state-of-the-art approximate adders using a cost function based on the energy, delay, area, and output quality and results indicate an average of 50% reduction in terms of the cost function compared to other approximateAdders.