Neuromorphic accelerators promise unparalleled energy efficiency and computational density for spiking neural networks, especially in wearable biomedical devices and neural prosthetics where power constraints are stringent. However, most existing platforms exhibit rigid architectures with limited configurability, restricting their adaptability to heterogeneous biological signals and diverse design objectives. To address these limitations, we present Flexi-NeurA--a flexible neuromorphic accelerator that unifies configurability and efficiency. Flexi-NeurA allows users to customize neuron models, network structures, and precision settings at design time. By pairing these design-time configurability features with a time-multiplexed and event-driven processing approach, Flexi-NeurA reduces the required hardware resources and total power while preserving high efficiency and low inference latency. Complementing this, we introduce Flex-plorer, a design-space exploration 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 distinct domains--biomedical auditory processing, dynamic vision sensor gesture recognition, and standard vision classification--demonstrate that the hardware/software co-framework successfully balances accuracy and power budgets for diverse applications. A 3-layer 256-128-10 fully connected network with LIF neurons mapped onto two processing cores achieves 96.23% accuracy on MNIST with 1.1 ms inference latency, utilizing only 1,623 logic cells, 7 BRAMs, and 111 mW of total power--demonstrating superior resource efficiency compared to SoTA hardware baselines.

翻译：神经形态加速器在脉冲神经网络中展现出无与伦比的能效与计算密度，尤其适用于功耗约束严苛的可穿戴生物医学设备及神经假体。然而，现有平台大多结构僵化、可配置性有限，限制了其对异质生物信号和多样化设计目标的适应性。为突破上述局限，我们提出Flexi-NeurA——一种兼具可配置性与高效能的柔性神经形态加速器。该加速器支持用户在设计阶段自定义神经元模型、网络结构及精度设置。通过将设计时配置特性与时域复用、事件驱动处理方式相结合，Flexi-NeurA在保持高能效和低推理延迟的同时，显著减少了硬件资源占用和总功耗。作为配套方案，我们引入Flex-plorer设计空间探索工具，基于用户定义的精度与资源使用权衡，为衰减因子、突触权重、膜电位等关键参数确定经济高效的定点精度。根据Flex-plorer流程所选配置生成对应的RTL代码。跨领域综合评估（涵盖生物医学听觉处理、动态视觉传感器手势识别及标准视觉分类任务）表明，该硬件/软件协同框架能有效平衡不同应用场景的精度与功耗预算。采用LIF神经元的3层256-128-10全连接网络映射至两个处理核心后，在MNIST数据集上实现96.23%的准确率、1.1毫秒推理延迟，仅消耗1623个逻辑单元、7个BRAM及111毫瓦总功耗——相较现有前沿硬件基线展现出卓越的资源效率。