- author = "Adnan Aquib Naseer and Rizwan A. Ashraf and Damian Dechev and Ronald F. DeMara",
- title = "Designing energy-efficient approximate adders using parallel genetic algorithms",
- booktitle = "SoutheastCon 2015",
- year = "2015",
- address = "Fort Lauderdale, FL, USA",
- month = "9-12 " # apr,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, parallelism, genetic algorithms, parallel , inexact arithmetic units, approximate computing,adder, variable accuracy, low power, adders, error distance,power consumption, process variation, delay, power reduction",
- isbn13 = "978-1-4673-7300-5",
-
URL = "
https://cal.ucf.edu/wp-content/uploads/2020/06/SECon-A-2015.pdf",
-
DOI = "
doi:10.1109/SECON.2015.7132970",
- size = "7 pages",
- abstract = "Approximate computing involves selectively reducing the number of transistors in a circuit to improve energy savings. Energy savings may be achieved at the cost of reduced accuracy for signal processing applications whereby constituent adder and multiplier circuits need not generate a precise output. Since the performance versus energy savings landscape is complex, we investigate the acceleration of the design of approximate adders using parallelised Genetic Algorithms (GAs). The fitness evaluation of each approximate adder is explored by the GA in a non-sequential fashion to automatically generate novel approximate designs within specified performance thresholds. This paper advances methods of parallelizing GAs and implements a new parallel GA approach for approximate multi-bit adder design. A speedup of approximately 1.6-fold is achieved using a quad-core Intel processor and results indicate that the proposed GA is able to find adders which consume 63:8percent less energy than accurate adders.",
-
notes = "Also known as \cite{7132970}
Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, USA",
