Machine Learning Using Approximate Computing

Approximate computation has emerged as a promising alternative to accurate computation, particularly for applications that can tolerate some degree of error without significant degradation of the output quality. This work analyzes the application of approximate computing for machine learning, specifically focusing on k-means clustering, one of the more widely used unsupervised machine learning algorithms. The k-means algorithm partitions data into k clusters, where k also denotes the number of centroids, with each centroid representing the center of a cluster. The clustering process involves assigning each data point to the nearest centroid by minimizing the within-cluster sum of squares (WCSS), a key metric used to evaluate clustering quality. A lower WCSS value signifies better clustering. Conventionally, WCSS is computed with high precision using an accurate adder. In this paper, we investigate the impact of employing various approximate adders for WCSS computation and compare their results against those obtained with an accurate adder. Further, we propose a new approximate adder (NAA) in this paper. To assess its effectiveness, we utilize it for the k-means clustering of some publicly available artificial datasets with varying levels of complexity, and compare its performance with the accurate adder and many other approximate adders. The experimental results confirm the efficacy of NAA in clustering, as NAA yields WCSS values that closely match or are identical to those obtained using the accurate adder. We also implemented hardware designs of accurate and approximate adders using a 28 nm CMOS standard cell library. The design metrics estimated show that NAA achieves a 37% reduction in delay, a 22% reduction in area, and a 31% reduction in power compared to the accurate adder. In terms of the power-delay product that serves as a representative metric for energy efficiency, NAA reports a 57% reduction compared to the accurate adder. In terms of the area-delay product that serves as a representative metric for design efficiency, NAA reports a 51% reduction compared to the accurate adder. NAA also outperforms several existing approximate adders in terms of design metrics while preserving clustering effectiveness.