NeuroFlex: Column-Exact ANN-SNN Co-Execution Accelerator with Cost-Guided Scheduling
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| Format: | Preprint |
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2025
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| _version_ | 1866908635928461312 |
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| author | Manjunath, Varun Ramesh, Pranav Srinivasan, Gopalakrishnan |
| author_facet | Manjunath, Varun Ramesh, Pranav Srinivasan, Gopalakrishnan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_05215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuroFlex: Column-Exact ANN-SNN Co-Execution Accelerator with Cost-Guided Scheduling Manjunath, Varun Ramesh, Pranav Srinivasan, Gopalakrishnan Neural and Evolutionary Computing Hardware Architecture 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. |
| title | NeuroFlex: Column-Exact ANN-SNN Co-Execution Accelerator with Cost-Guided Scheduling |
| topic | Neural and Evolutionary Computing Hardware Architecture |
| url | https://arxiv.org/abs/2511.05215 |