{"ID":2844923,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05215","arxiv_id":"2511.05215","title":"NeuroFlex: Column-Exact ANN-SNN Co-Execution Accelerator with Cost-Guided Scheduling","abstract":"NeuroFlex is a column-level accelerator that co-executes artificial and spiking neural networks to minimize energy-delay product on sparse edge workloads with competitive accuracy. The design extends integer-exact QCFS ANN-SNN conversion from layers to independent columns. It unifies INT8 storage with on-the-fly spike generation using an offline cost model to assign columns to ANN or SNN cores and pack work across processing elements with deterministic runtime. Our cost-guided scheduling algorithm improves throughput by 16-19% over random mapping and lowers EDP by 57-67% versus a strong ANN-only baseline across VGG-16, ResNet-34, GoogLeNet, and BERT models. NeuroFlex also delivers up to 2.5x speedup over LoAS and 2.51x energy reduction over SparTen. These results indicate that fine-grained and integer-exact hybridization outperforms single-mode designs on energy and latency without sacrificing accuracy.","short_abstract":"NeuroFlex is a column-level accelerator that co-executes artificial and spiking neural networks to minimize energy-delay product on sparse edge workloads with competitive accuracy. The design extends integer-exact QCFS ANN-SNN conversion from layers to independent columns. It unifies INT8 storage with on-the-fly spike...","url_abs":"https://arxiv.org/abs/2511.05215","url_pdf":"https://arxiv.org/pdf/2511.05215v1","authors":"[\"Varun Manjunath\",\"Pranav Ramesh\",\"Gopalakrishnan Srinivasan\"]","published":"2025-11-07T13:12:24Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AR\"]","methods":"[]","has_code":false}