ML-based Load Value Approximator for Efficient Multimedia Processing
Alain Aoun, Mahmoud Masadeh, Sofiène Tahar · ACM Transactions on Multimedia Computing Communications and Applications · 2025
Approximate computing (AC) has gained traction as an alternative computing method for energy-efficient processing. This article proposes the exploitation of AC to address the memory wall. The proposed model predicts the memory load value using machine learning (ML). Subsequently, the ML model is a load value approximator (LVA) where the generated value is accepted as-is. The proposed LVA was tested under various approximate conditions, where 50% to 95% of the load instructions were approximated using a set of multimedia applications. The memory access operation using the proposed LVA was more than \(6\times\) faster in multiple cases. Additionally, the applications tested ran on average \(1.83\times\) faster. The peak signal-to-noise ratio (PSNR) exceeded 37 dB in several scenarios. The average normalized mean absolute error (NMAE) was 4.54%.