{"ID":5438823,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T11:20:51.789462812Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31514","arxiv_id":"2606.31514","title":"MINT: Dynamic-Precision CNN Inference with MSDF Digit-Serial Arithmetic on FPGA","abstract":"We present MINT, a dynamic-precision CNN inference accelerator based on left-to-right (LR) arithmetic. LR arithmetic computes in most-significant-digit-first manner and exposes useful partial results early so that the computation can be terminated once the desired precision is achieved. At the core, there is a MSDF serial-parallel inner-product unit, which uses redundant signed-digit representation to compute each convolution window. A budget-constrained greedy search profiles all convolution layers from INT2 to INT7 and selects the lowest precision per layer while constraining total accuracy loss to within 2\\% of the INT8 baseline for VGG-16 and ResNet-18 networks. The design is synthesized on a Xilinx Zynq-7020 at \\SI{200}{\\mega\\hertz}, and uses 5.64 average bits for VGG-16 and 6.04 for ResNet-18, while achieving 19.86 GOPS and 29.51 GOPS/W on VGG-16, and 18.86 GOPS and 26.40 GOPS/W on ResNet-18. This corresponds to 32.6\\% and 26.0\\% higher throughput and 82.10\\% and 62.90\\% higher energy efficiency than INT8 with only 1.81\\% and 1.96\\% drops relative to the INT8 baseline. Compared with representative prior FPGA CNN accelerators considered in this study, MINT delivers the highest energy efficiency among the listed VGG-16 and ResNet-18 designs on Zynq-7020 platform.","short_abstract":"We present MINT, a dynamic-precision CNN inference accelerator based on left-to-right (LR) arithmetic. LR arithmetic computes in most-significant-digit-first manner and exposes useful partial results early so that the computation can be terminated once the desired precision is achieved. At the core, there is a MSDF ser...","url_abs":"https://arxiv.org/abs/2606.31514","url_pdf":"https://arxiv.org/pdf/2606.31514v1","authors":"[\"Muhammad Usman\",\"Malik Zohaib Nisar\",\"Florian Aschauer\",\"Dorit Merhof\"]","published":"2026-06-30T11:29:23Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"math.LO\",\"math.OC\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}