Affiliation:
1. Interuniversity Microelectronics Centre (IMEC), Belgium
2. cfaed, Technische Universität Dresden, Germany
Abstract
Approximate computing (AxC) provides the scope for achieving disproportionate gains in a system’s power, performance, and area (PPA) metrics by leveraging an application’s inherent error-resilient behavior (BEHAV). Trading computational accuracy for performance gains makes AxC an attractive proposition for implementing computationally complex AI/ML-based applications on resource-constrained embedded systems. The growing diversity of application domains using AI/ML has also led to the increasing usage of FPGA-based embedded systems. However, implementing AxC for FPGAs has primarily been limited to the post-processing of ASIC-optimized approximate operators (AxOs). This approach usually involves selecting from a set of AxOs that have been optimized for a gate-based implementation in an ASIC. While such an approach does allow leveraging existing knowledge of ASIC-based AxO design, it limits the scope for considering the challenges and opportunities associated with FPGA’s LUT-based computation structures. Similarly, the few works considering the LUT-based computing for AxO design use generic optimization approaches that do not allow integrating problem-specific prior knowledge—empirical and/or statistical. To this end, we propose a novel tree search-based approach to AxO synthesis for FPGAs. Specifically, we present a design methodology using Monte Carlo Tree Search (MCTS)-based search tree traversal that allows the designer to integrate statistical data, such as correlation, into the AxOs optimization. With the proposed methods, we report improvements over standard MCTS algorithm-based results as well as improved hypervolume for both operator-level and application-specific DSE, compared to state-of-the-art design methodologies.
Funder
Deutsche Forschungsgemeinschaft
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Software
Cited by
1 articles.
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