Biblio

Approximate computing emerged as a key alternative for trading off accuracy against energy efficiency and area reduction. Error-tolerant applications, such as multimedia processing, machine learning, and signal processing, can process the information with lower-than-standard accuracy at the circuit level while still fulfilling a good and acceptable service quality at the application level. Adaptive filtering-based systems have been demonstrating high resiliency against hardware errors due to their intrinsic self-healing characteristic. This paper investigates the design space exploration of arithmetic approximations in a Very Large-Scale Integration (VLSI) harmonic elimination (HE) hardware architecture based on Least Mean Square (LMS) adaptive filters. We evaluate the Pareto front of the area- and power versus quality curves by relaxing the arithmetic precision and by adopting both approximate multipliers (AxMs) in combination with approximate adders (AxAs). This paper explores the benefits and impacts of the Dynamic Range Unbiased (DRUM), Rounding-based Approximate (RoBA), and Leading one Bit-based Approximate (LoBA) multipliers in the power dissipation, circuit area, and quality of the VLSI HE architectures. Our results highlight the LoBA 0 as the most efficient AxM applied in the HE architecture. We combine the LoBA 0 with Copy and LOA AxAs with variations in the approximation level (L). Notably, LoBA 0 and LOA with \$L=6\$ resulted in savings of 43.7% in circuit area and 45.2% in power dissipation, compared to the exact HE, which uses multiplier and adder automatically selected by the logic synthesis tool. Finally, we demonstrate that the best hardware architecture found in our investigation successfully eliminates the contaminating spurious noise (i.e., 60 Hz and its harmonics) from the signal.