Using Machine Learning for Quality Configurable Approximate Computing
Mahmoud Masadeha, Osman Hasanb and Sofiène Taharc
Concordia University, Montreal, Quebec, Canada
am_masa@ece.concordia.ca
bo_hasan@ece.concordia.ca
ctahar@ece.concordia.ca
ABSTRACT
Approximate computing (AC) is a nascent energyefficient computing paradigm for error-resilient applications. However, the quality control of AC is quite challenging due to its input-dependent nature. Existing solutions fail to address finegrained input-dependent controlled approximation. In this paper,we propose an input-aware machine learning based approach for the quality control of AC. For illustration purposes, we use 20 configurations of 8-bit approximate multipliers.We evaluate these designs for all combinations of possible input data. Then, we use machine learning algorithms to efficiently make predictive decisions for the quality control of the target approximate application, based on experimentally collected training data. The key benefits of the proposed approach include: (1) fine-grained input-dependent approximation, (2) no missed approximation opportunities, (3) no rollback recovery overhead, (4) applicable to any approximate computation with error-tolerant components, and (5) flexibility in adapting various error metrics.
