NEURAL: An Elastic Neuromorphic Architecture with Hybrid Data-Event Execution and On-the-fly Attention Dataflow
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| Format: | Preprint |
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2025
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| _version_ | 1866918143697354752 |
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| author | Chen, Yuehai Merchant, Farhad |
| author_facet | Chen, Yuehai Merchant, Farhad |
| contents | Spiking neural networks (SNNs) have emerged as a promising alternative to artificial neural networks (ANNs), offering improved energy efficiency by leveraging sparse and event-driven computation. However, existing hardware implementations of SNNs still suffer from the inherent spike sparsity and multi-timestep execution, which significantly increase latency and reduce energy efficiency. This study presents NEURAL, a novel neuromorphic architecture based on a hybrid data-event execution paradigm by decoupling sparsity-aware processing from neuron computation and using elastic first-in-first-out (FIFO). NEURAL supports on-the-fly execution of spiking QKFormer by embedding its operations within the baseline computing flow without requiring dedicated hardware units. It also integrates a novel window-to-time-to-first-spike (W2TTFS) mechanism to replace average pooling and enable full-spike execution. Furthermore, we introduce a knowledge distillation (KD)-based training framework to construct single-timestep SNN models with competitive accuracy. NEURAL is implemented on a Xilinx Virtex-7 FPGA and evaluated using ResNet-11, QKFResNet-11, and VGG-11. Experimental results demonstrate that, at the algorithm level, the VGG-11 model trained with KD improves accuracy by 3.20% on CIFAR-10 and 5.13% on CIFAR-100. At the architecture level, compared to existing SNN accelerators, NEURAL achieves a 50% reduction in resource utilization and a 1.97x improvement in energy efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NEURAL: An Elastic Neuromorphic Architecture with Hybrid Data-Event Execution and On-the-fly Attention Dataflow Chen, Yuehai Merchant, Farhad Hardware Architecture Spiking neural networks (SNNs) have emerged as a promising alternative to artificial neural networks (ANNs), offering improved energy efficiency by leveraging sparse and event-driven computation. However, existing hardware implementations of SNNs still suffer from the inherent spike sparsity and multi-timestep execution, which significantly increase latency and reduce energy efficiency. This study presents NEURAL, a novel neuromorphic architecture based on a hybrid data-event execution paradigm by decoupling sparsity-aware processing from neuron computation and using elastic first-in-first-out (FIFO). NEURAL supports on-the-fly execution of spiking QKFormer by embedding its operations within the baseline computing flow without requiring dedicated hardware units. It also integrates a novel window-to-time-to-first-spike (W2TTFS) mechanism to replace average pooling and enable full-spike execution. Furthermore, we introduce a knowledge distillation (KD)-based training framework to construct single-timestep SNN models with competitive accuracy. NEURAL is implemented on a Xilinx Virtex-7 FPGA and evaluated using ResNet-11, QKFResNet-11, and VGG-11. Experimental results demonstrate that, at the algorithm level, the VGG-11 model trained with KD improves accuracy by 3.20% on CIFAR-10 and 5.13% on CIFAR-100. At the architecture level, compared to existing SNN accelerators, NEURAL achieves a 50% reduction in resource utilization and a 1.97x improvement in energy efficiency. |
| title | NEURAL: An Elastic Neuromorphic Architecture with Hybrid Data-Event Execution and On-the-fly Attention Dataflow |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2509.15036 |