On the Design of an Energy Efficient Digital IIR A-Weighting Filter Using Approximate Multiplication

Abstract

This paper presents a new A-weighting filter’s design and explores the potential of using approximate multiplication for low-power digital A-weighting filter implementation. It presents a thorough analysis of the effects of approximate multiplication, coefficient quantization, the order of first-order sections in the filter’s cascade, and zero-pole pairings on the frequency response of the digital A-weighting filter. The proposed A-weighting filter was implemented as a sixth-order IIR filter using approximate odd radix-4 multipliers. The proposed filter was synthesized (Verilog to GDS) using the Nangate45 cell library, and MATLAB simulations were performed to verify the designed filter’s magnitude response and performance. Synthesis results indicate that the proposed design achieves nearly 70% reduction in energy (power-delay product) with a negligible deviation of the frequency response from the floating-point implementation. Experiments on acoustic noise suggest that the proposed digital A-weighting filter can be deployed in environmental noise measurement applications without any notable performance degradation.

Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing

“…First, a recent state-of-the-art digital FIR filter using data-driven clock gating and multibit flip-flops combined achieved 22% to 25% power reduction compared to that using a conventional design [3]. Secondly, in another very recent study, a novel design for an energyefficient IIR digital filter achieved nearly 63% reduction in energy with a negligible deviation of the frequency response from the standard implementation [1]. Finally, a low power reconfigurable FIR digital filter based on dual mode operation achieved power savings up to 37.97% in simulations using speech signal processing, similar to the simulations in this section [32].…”

“…In this section we develop the low-power (LP) solution to the approximate filtering problem summarized in (1). The LP solution provides a method for computing the estimateN * LP of the optimal filter order N * based on low-power operations.…”

“…Adaptive filtering and energy efficiency has been a topic of great interest in recent years. For example, the interest in reducing the power consumption of digital filters used in edge computing and sensor networks is growing rapidly [1]. A fundamental tradeoff between power consumption and accuracy has been utilized to determine energy-optimum design parameters for deep in-memory architectures for efficient hardware realizations of machine learning algorithms [2].…”

“…Recently, excellent research has been accomplished in the area of incremental refinement structures for approximate signal processing in the context of power-efficient approximate multiplication for applications in which exact computation is not necessary [4][5][6][7][8]. The design of an energy efficient digital IIR filter using approximate multiplication, with a similar objective to the IIR filters presented in this paper, is presented in the research by Pilipović et al [1]. Furthermore, approximate computing represents another recent innovation for minimizing power consumption via a variety of methods for reducing arithmetic activity [9][10][11][12].…”

The Importance of Energy Efficiency in Engineering and Informatics

“…Another part of this movement is digital filtering using data-driven clock gating and multibit flip-flops combined, which has achieved 22% to 25% power reduction compared to that using a conventional design [11]. In another study, an energy efficient digital filter achieved nearly 70% reduction in energy with a negligible deviation of the frequency response from the standard implementation [12]. As a third example, in simulations using speech signals, a low power reconfigurable FIR digital filter based on dual mode operation achieved power savings up to 37.97% [13].…”