Notice
This record has been edited as far as possible, missing data will be added when the version of record is issued.
AxOCS: Scaling FPGA-Based Approximate Operators Using Configuration Supersampling
Metadata only
Date
2024Type
- Journal Article
ETH Bibliography
yes
Altmetrics
Abstract
The rising usage of AI/ML-based processing across application domains has exacerbated the need for low-cost ML implementation, specifically for resource-constrained embedded systems. To this end, approximate computing, an approach that explores the power, performance, area (PPA), and behavioral accuracy (BEHAV) trade-offs, has emerged as a possible solution for implementing embedded machine learning. Due to the predominance of MAC operations in ML, designing platform-specific approximate arithmetic operators forms one of the major research problems in approximate computing. Recently, there has been a rising usage of AI/ML-based design space exploration techniques for implementing approximate operators. However, most of these approaches are limited to using ML-based surrogate functions for predicting the PPA and BEHAV impact of a set of related design decisions. While this approach leverages the regression capabilities of ML methods, it does not exploit the more advanced approaches in ML. To this end, we propose, a methodology for designing approximate arithmetic operators through ML-based supersampling. Specifically, we present a method to leverage the correlation of PPA and BEHAV metrics across operators of varying bit-widths for generating larger bit-width operators. The proposed approach involves traversing the relatively smaller design space of smaller bit-width operators and employing its associated Design-PPA-BEHAV relationship to generate initial solutions for metaheuristics-based optimization for larger operators. The experimental evaluation of for FPGA-optimized approximate operators shows that the proposed approach significantly improves the quality-resulting hypervolume for multi-objective optimization-of 8 x 8 signed approximate multipliers. Show more
Publication status
publishedExternal links
Journal / series
IEEE Transactions on Circuits and Systems I: Regular PapersPublisher
IEEESubject
AI-based design space exploration; approximate computing; arithmetic operator design; circuit synthesisMore
Show all metadata
ETH Bibliography
yes
Altmetrics