Neuromorphic accelerators promise unparalleled energy efficiency and computational density for spiking neural networks (SNNs), especially in edge intelligence applications. However, most existing platforms exhibit rigid architectures with limited configurability, restricting their adaptability to heterogeneous workloads and diverse design objectives. To address these limitations, we present Flexi-NeurA -- a parameterizable neuromorphic accelerator (core) that unifies configurability, flexibility, and efficiency. Flexi-NeurA allows users to customize neuron models, network structures, and precision settings at design time. By pairing these design-time configurability and flexibility features with a time-multiplexed and event-driven processing approach, Flexi-NeurA substantially reduces the required hardware resources and total power while preserving high efficiency and low inference latency. Complementing this, we introduce Flex-plorer, a heuristic-guided design-space exploration (DSE) tool that determines cost-effective fixed-point precisions for critical parameters -- such as decay factors, synaptic weights, and membrane potentials -- based on user-defined trade-offs between accuracy and resource usage. Based on the configuration selected through the Flex-plorer process, RTL code is configured to match the specified design. Comprehensive evaluations across MNIST, SHD, and DVS benchmarks demonstrate that the Flexi-NeurA and Flex-plorer co-framework achieves substantial improvements in accuracy, latency, and energy efficiency. A three-layer 256--128--10 fully connected network with LIF neurons mapped onto two processing cores achieves 97.23% accuracy on MNIST with 1.1~ms inference latency, utilizing only 1,623 logic cells, 7 BRAMs, and 111~mW of total power -- establishing Flexi-NeurA as a scalable, edge-ready neuromorphic platform.

翻译：神经形态加速器为脉冲神经网络（SNNs）带来了无与伦比的能效和计算密度，尤其在边缘智能应用中前景广阔。然而，现有的大多数平台架构僵化，可配置性有限，限制了其对异构工作负载和多样化设计目标的适应性。为应对这些局限，本文提出了Flexi-NeurA——一种参数化的神经形态加速器（核心），它集可配置性、灵活性和高效性于一体。Flexi-NeurA允许用户在设计时自定义神经元模型、网络结构和精度设置。通过将这种设计时的可配置性与灵活性特征，与时分复用和事件驱动的处理方法相结合，Flexi-NeurA在保持高效率与低推理延迟的同时，显著减少了所需的硬件资源和总功耗。作为补充，我们引入了Flex-plorer，这是一种启发式引导的设计空间探索工具，它根据用户在精度和资源使用之间定义的权衡，为关键参数（如衰减因子、突触权重和膜电位）确定高性价比的定点精度。通过Flex-plorer流程选定的配置，会生成与之匹配的RTL代码。在MNIST、SHD和DVS基准测试上的综合评估表明，Flexi-NeurA与Flex-plorer协同框架在精度、延迟和能效方面均取得了显著提升。一个具有LIF神经元的三层全连接网络（256–128–10）映射到两个处理核心上，在MNIST数据集上实现了97.23%的准确率，推理延迟仅为1.1毫秒，仅消耗1,623个逻辑单元、7个BRAM和111毫瓦的总功耗——这确立了Flexi-NeurA作为一个可扩展、适用于边缘场景的神经形态平台的地位。