{"ID":2873829,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06162","arxiv_id":"2509.06162","title":"An Improved Template for Approximate Computing","abstract":"Deploying neural networks on edge devices entails a careful balance between the energy required for inference and the accuracy of the resulting classification. One technique for navigating this tradeoff is approximate computing: the process of reducing energy consumption by slightly reducing the accuracy of arithmetic operators. In this context, we propose a methodology to reduce the area of the small arithmetic operators used in neural networks - i.e., adders and multipliers - via a small loss in accuracy, and show that we improve area savings for the same accuracy loss w.r.t. the state of the art. To achieve our goal, we improve on a boolean rewriting technique recently proposed, called XPAT, where the use of a parametrisable template to rewrite circuits has proved to be highly beneficial. In particular, XPAT was able to produce smaller circuits than comparable approaches while utilising a naive sum of products template structure. In this work, we show that template parameters can act as proxies for chosen metrics and we propose a novel template based on parametrisable product sharing that acts as a close proxy to synthesised area. We demonstrate experimentally that our methodology converges better to low-area solutions and that it can find better approximations than both the original XPAT and two other state-of-the-art approaches.","short_abstract":"Deploying neural networks on edge devices entails a careful balance between the energy required for inference and the accuracy of the resulting classification. One technique for navigating this tradeoff is approximate computing: the process of reducing energy consumption by slightly reducing the accuracy of arithmetic...","url_abs":"https://arxiv.org/abs/2509.06162","url_pdf":"https://arxiv.org/pdf/2509.06162v2","authors":"[\"Morteza Rezaalipour\",\"Francesco Costa\",\"Marco Biasion\",\"Rodrigo Otoni\",\"George A. Constantinides\",\"Laura Pozzi\"]","published":"2025-09-07T18:09:59Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AR\"]","methods":"[]","has_code":false}