{"ID":2849804,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.19900","arxiv_id":"2601.19900","title":"Flexible Bit-Truncation Memory for Approximate Applications on the Edge","abstract":"Bit truncation has demonstrated great potential to enable run-time quality-power adaptive data storage, thereby optimizing the power/energy efficiency of approximate applications and supporting their deployment in edge environments. However, existing bit-truncation memories require custom designs for a specific application. In this paper, we present a novel bit-truncation memory with full adaptation flexibility, which can truncate any number of data bits at run time to meet different quality and power trade-off requirements for various approximate applications. The developed bit-truncation memory has been applied to two representative data-intensive approximate applications: video processing and deep learning. Our experiments show that the proposed memory can support three different video applications (including luminance-aware, content-aware, and region-of-interest-aware) with enhanced power efficiency (up to 47.02% power savings) as compared to state-of-the-art. In addition, the proposed memory achieves significant (up to 51.69%) power savings for both baseline and pruned lightweight deep learning models, respectively, with a low implementation cost (2.89% silicon area overhead).","short_abstract":"Bit truncation has demonstrated great potential to enable run-time quality-power adaptive data storage, thereby optimizing the power/energy efficiency of approximate applications and supporting their deployment in edge environments. However, existing bit-truncation memories require custom designs for a specific applica...","url_abs":"https://arxiv.org/abs/2601.19900","url_pdf":"https://arxiv.org/pdf/2601.19900v1","authors":"[\"William Oswald\",\"Mario Renteria-Pinon\",\"Md. Sajjad Hossain\",\"Kyle Mooney\",\"Md. Bipul Hossain\",\"Destinie Diggs\",\"Yiwen Xu\",\"Mohamed Shaban\",\"Jinhui Wang\",\"Na Gong\"]","published":"2025-10-27T18:26:03Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}