Abstract
Since most applications amenable to approximate computing involve large data paths, it is essential to optimize accelerators, in addition to designing individual arithmetic modules. To this end, we need to minimize the error propagated through different arithmetic modules. With this motivation, we propose Self-Compensating Accelerators (SeCAs), that are constructed by combining different approximate arithmetic modules in such a way that the approximation error is completely or partially canceled within the accelerator data path, and thus the cumulative error at the output is reduced. For illustration purposes, we use block-based approximate adders and recursive multipliers. Simulation results show that the proposed SeCAs help achieve significant benefits in accuracy, while keeping other performance measures, i.e., speed, area and power, unaffected. This quality gain can be exploited in two ways: (1) employing SeCAs when high error cannot be afforded, yet high area/performance/power efficiency is required; (2) using more aggressive approximations to achieve even further efficiency increase.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
