ABSTRACT This study introduces an OpenMP construct designed to simplify and unify the integration of approximate computing techniques into shared‐memory parallel programs. Approximate Computing leverages the inherent error tolerance of many applications to trade computational accuracy for gains in performance and energy efficiency. Unlike prior OpenMP‐based approximation proposals, which typically focus on a single technique, our constructor provides a unified and portable interface that integrates multiple software approximation strategies. The proposed construct provides native support for software Approximate Computing strategies, including task dropping , loop perforation , floating‐point relaxation , and memoization , enabling developers to selectively apply these techniques with minimal code disruption. The experimental evaluation across a set of representative benchmark applications reveals performance improvements of up to 1272.90%, with a degradation of 83.3% accuracy and energy savings reaching up to 99.34% in specific configurations. These results underscore the viability of Approximate Computing alongside parallel programming models and open the door for further exploration of compiler and runtime support for controlled inaccuracy in high‐performance computing.

Approximate memories provide energy savings or performance improvements at the cost of occasional errors in stored data. Applications that tolerate errors on their data profit from this trade-off by controlling these errors to not affect critical data. This control usually involves programmer intervention with annotations in the source code. To avoid annotations, some techniques protect critical data that are common on many applications, isolating specific memory regions from errors. In this work, we propose and explore alternatives for the protection of application critical data by managing a supervisor execution environment with an approximate memory system. We expose only dynamically allocated data to errors with secure data manipulation through an approximate allocation scheme that divide stored data based on the approximation of the heap area. We evaluate 6 applications with different data access profiles and obtain up to 20% of energy savings.

Representing the interaction between accurate and approximate hardware modules at the architecture level is essential to understand the impact of Approximate Computing in a general-purpose computing scenario. However, extensive effort is required to model approximations into a baseline instruction-level simulator and collect its execution metrics. In this work, we present the AxPIKE ISA simulation environment, a tool that allows designers to inject models of hardware approximation at the instruction level and evaluate their impact on the quality of results. AxPIKE embeds a high-level representation of a RISC-V system and produces a dedicated control mechanism, that allows the simulated software to manage the approximate behavior of compatible execution scenarios. The environment also provides detailed execution statistics that are forwarded to dedicated tools for energy accounting. We apply the AxPIKE environment to inject integer multiplication and memory access approximations into different applications and demonstrate how the generated statistics are translated into energy-quality trade-offs.

Approximate systems are designed to offer improved efficiency with potentially reduced quality of results. Quality of output in these systems is typically quantified in comparison to a precise result using metrics such as RMSE, MAE, PSNR, or application-specific metrics such as structural similarity of images (SSIM). Furthermore, systems are typically designed to maximize efficiency for a given minimum quality requirement. It is often difficult to determine what this quality requirement should be for an application, let alone a system. Thus, a fixed quality requirement may be overly conservative, and leave optimization opportunities on the table. In this work, we present a different approach to evaluate approximate systems based on the usefulness of results instead of quality. Our method qualitatively determines the acceptability of approximate results within different processing pipelines. To demonstrate the method, we implement three image and signal processing applications featuring scenarios of image classification, image recognition, and frequency estimation. Our results show that designing approximate systems to guarantee acceptability can produce up to 20% more valid results than the conservative quality thresholds commonly adopted in the literature, allowing for higher error rates and, consequently, lower energy cost.

Approximate Computing offers enhanced energy efficiency by exploring quality relaxation on applications. Application-agnostic hardware-level techniques can provide high benefits under certain scenarios, but their integration on a general-purpose architecture presents novel control challenges. We present Risk-5, an extension of the RISC-V architecture that implements control mechanisms to orchestrate multiple coexisting approximation techniques within an architecture. In Risk-5, approximate hardware capabilities are exposed to software through identification registers, data structures, and drivers that describe the nature and configuration parameters for each approximate design. This allows the software stack to control what and how much is approximated in an application. Control options range from activating or deactivating a certain approximation (e.g., approximating ALU operations), to configuring allowable error levels (e.g., for a configurable FPU), and configuring operation parameters that may lead to probabilistic errors (e.g., setting the refresh rate for an approximate SDRAM). Approximations may be dynamically configured and combined at runtime, allowing for simplified design space exploration. Finally, supervisor- and machine-level control allows for the use of certain approximations without requiring changes to applications. In this article, we discuss the implementation of different classes of approximation techniques, detailing and evaluating how they interact with each other. Risk-5 and the selected approximations are demonstrated in the functional level in a RISC-V ISA simulator augmented with an approximate computing framework. Our experiments evaluate how six applications from different computing domains behave when subjected to a combination of approximation techniques. Our results show how Risk-5 can bridge the gap between software and hardware approximations, allowing designers to easily evaluate energy-quality tradeoffs.