Inexact Circuits or circuits in which the accuracy of the output can be traded for energy or delay savings, have been receiving increasing attention of late due to invariable inaccuracies in designs as Moore's law approaches the low nanometer range, and a concomitant growing desire for ultra low energy systems. In this paper, we present a novel designlevel technique called probabilistic pruning to realize inexact circuits. Unlike the previous techniques in literature which relied mostly on some form of scaling of operational parameters such as the supply voltage (V dd ) to achieve energy and accuracy tradeoffs, our technique uses pruning of portions of circuits having a lower probability of being active, as the basis for performing architectural modifications resulting in significant savings in energy, delay and area. Our approach yields more savings when compared to any of the conventional voltage scaling schemes, for similar error values. Extensive simulations using this pruning technique in a novel logic synthesis based CAD framework on various architectures of 64-bit adders demonstrate that normalized gains as great as 2X-7.5X in the Energy-DelayArea product can be obtained, with a relative error percentage as low as 10 −6 % up to 10%, when compared to corresponding conventionally correct designs.
We develop a theoretical foundation to characterize a novel methodology for low energy and high performance dsp for embedded computing. Computing elements are operated at a frequency higher than that permitted by a conventionally correct circuit design, enabling a trade-off between error that is deliberately introduced, and the energy consumed. Similar techniques considered previously were relevant to deeply scaled future technology generations. Our work extends this idea to be applicable to current-day designs through: (i) a mathematically rigorous foundation characterizing a tradeoff between energy consumed and the quality of solution, and (ii) a means of achieving this trade off through very aggressive voltage scaling beyond that of a conventionally designed circuit. Through our "cmos inspired" mathematical model, we show that our approach is better (by an exponential factor) than the conventional uniform voltage scaling approach for comparable computational speed or performance. We further establish through experimental study that a similar improvement by a factor of 3.4x to the snr over conventional voltage-scaled approaches can be achieved in the context of the ubiquitous discrete Fourier transform.
The domain of inexact circuit design, in which accuracy of the circuit can be exchanged for substantial cost (energy, delay, and/or area) savings, has been gathering increasing prominence of late owing to a growing desire for reducing energy consumption of the systems, particularly in the domain of embedded and (portable) multimedia applications. Most of the previous approaches to realizing inexact circuits relied on scaling of circuit parameters (such as supply voltage) taking advantage of an application’s error tolerance to achieve the cost and accuracy trade-offs, thus suffering from acute drawbacks of considerable implementation overheads that significantly reduced the gains. In this article, two novel design approaches called Probabilistic Pruning and Probabilistic Logic Minimization are proposed to realize inexact circuits with zero hardware overhead.Extensive simulations on various architectures of critical datapath elements demonstrate that each of the techniques can independently achieve normalized gains as large as 2x--9.5x in energy-delay-area product for relative error magnitude as low as 10 − 4% --8% compared to corresponding conventional correct circuits.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.