We present a Cortical Neural Pool (CNP) architecture featuring a high-speed, resource-efficient CORDIC based Hodgkin-Huxley (RCHH) neuron model. Unlike shared CORDIC-based DNN approaches, the proposed neuron leverages modular and performance-optimised CORDIC stages with a latency-area trade-off. We introduce a novel Constraint-Aware Modular Parallelism (CAMP) with Precision & Stability handling to leverage maximum speedup and utilisation of hardware through hardware software co-design. The FPGA implementation of the RCHH neuron shows 24.5% LUT reduction and 35.2% improved speed, compared to SoTA designs, with 70% better normalised root mean square error (NRMSE). Furthermore, the CNP exhibits 2.85x higher throughput (12.69 GOPS) than a functionally equivalent CORDIC-based DNN engine, with only a 0.35% accuracy drop relative to the DNN counterpart on the MNIST dataset. The overall results indicate that the design shows biologically accurate, low-resource spiking neural network implementations for resource-constrained edge AI applications. The reproducibility codes are publicly available at https://github.com/mukullokhande99/CNP RCHH, facilitating rapid integration and further development by researchers.

翻译：我们提出了一种皮层神经池（CNP）架构，其采用基于高速、资源高效的CORDIC的霍奇金-赫胥黎（RCHH）神经元模型。与基于共享CORDIC的深度神经网络方法不同，所提出的神经元利用具有延迟-面积权衡的模块化且性能优化的CORDIC级。我们引入了一种新颖的约束感知模块化并行（CAMP）方法，结合精度与稳定性处理，通过硬件软件协同设计实现最大加速比和硬件利用率。RCHH神经元的FPGA实现显示，与最先进设计相比，LUT减少24.5%，速度提升35.2%，归一化均方根误差（NRMSE）改善70%。此外，该CNP在MNIST数据集上展现出比功能等效的基于CORDIC的深度神经网络引擎高2.85倍的吞吐量（12.69 GOPS），而相对于深度神经网络对应模型的精度仅下降0.35%。总体结果表明，该设计为资源受限的边缘AI应用提供了生物精确、低资源的脉冲神经网络实现。可复现代码已公开于https://github.com/mukullokhande99/CNP_RCHH，便于研究人员快速集成和进一步开发。