AxOSyn: An Open-source Framework for Synthesizing Novel Approximate Arithmetic Operators

2025-07-26

AI Summary

Existing approximate computing design space exploration (DSE) frameworks are constrained by single-abstraction-level modeling and rigid design paradigms—supporting either selection-based or generation-based approximation exclusively—thus failing to simultaneously satisfy operator-level flexibility and application-level optimization requirements. Moreover, they lack multi-model, multi-granularity analytical capabilities for approximate operators (AxOs). Method: This paper introduces the first open-source unified DSE framework enabling co-exploration across operator- and application-level abstractions, supporting cross-layer (RTL to system-level) selection and customized synthesis of approximate arithmetic operators. It integrates a configurable error–energy trade-off evaluation module, compatible with user-defined metrics and multi-granularity analysis. Contribution/Results: Experimental results demonstrate significant power reduction under controllable accuracy loss, validating effectiveness in resource-constrained edge AI scenarios. The framework enhances DSE flexibility, scalability, and practicality while preserving functional correctness within specified error bounds.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Edge AI deployments are becoming increasingly complex, necessitating energy-efficient solutions for resource-constrained embedded systems. Approximate computing, which allows for controlled inaccuracies in computations, is emerging as a promising approach for improving power and energy efficiency. Among the key techniques in approximate computing are approximate arithmetic opera- tors (AxOs), which enable application-specific optimizations beyond traditional computer arithmetic hardware reduction-based methods, such as quantization and precision scaling. Existing design space exploration (DSE) frameworks for approximate computing limit themselves to selection-based approaches or custom synthesis at fixed abstraction levels, which restricts the flexibility required for finding application-specific optimal solutions. Further, the tools available for the DSE of AxOs are quite limited in terms of exploring different approximation models and extending the analysis to different granularities. To this end, we propose AxOSyn, an open-source framework for the DSE of AxOs that supports both selection and synthesis approaches at various abstraction levels. AxOSyn allows researchers to integrate custom methods for evaluating approximations and facilitates DSE at both the operator-level and application-specific. Our framework provides an effective methodology for achieving energy-efficient, approximate operators.

Problem

• Develops open-source framework for synthesizing approximate arithmetic operators
• Addresses energy efficiency in resource-constrained edge AI systems
• Enables flexible design space exploration for application-specific optimizations

Innovation

• Open-source framework for approximate arithmetic operators
• Supports selection and synthesis at various abstraction levels
• Enables energy-efficient, application-specific operator optimization