Heuristics for Energy-Efficient Instruction-Level Approximate Computing

• Gregório K. Neto UFMS
• Felipe Sovernigo UFMS
• Daniela Catelan UFMS
• Ricardo Santos UFMS
• Liana Duenha UFMS

Resumo

Approximate Computing (AC) has emerged as a promising paradigm by enabling the execution of operations with reduced precision in scenarios where minor errors are acceptable. AC has shown efficiency in domains such as image processing, machine learning, and communication systems, which exhibit a natural tolerance to inaccuracy without significantly degrading outcomes. This work investigates the benefits of AC in improving energy savings across a range of applications. Specifically, this paper presents heuristics that explore the design space of applications to apply instruction-level approximate computation. The vast design space renders the combinatorial optimization problem computationally infeasible; therefore, a branch-and-bound heuristic has been designed to prune the design space, enabling a feasible exploration of the trade-off between accuracy and power consumption. An automated infrastructure is also proposed to guide the selection of approximate instruction sets in error-tolerant scenarios, aiming to achieve energy reduction targets while meeting user-defined accuracy constraints.

Palavras-chave: Accuracy, Sensitivity, Power demand, Source coding, Scalability, Approximate computing, Energy efficiency, Reproducibility of results, Space exploration, Computational efficiency, Design space exploration, Branch-And-Bound, Brute Force, Region-Wide heuristic, RISC-V instruction-set