A Low-Latency and High-Accuracy Dual-Mode Neuron Design for Accelerating Neurological Diseases Simulation and Analysis

Biological realism and computational efficiency are crucial for modeling neurological diseases with spiking neural networks (SNNs), as biological realism is necessary for observing neuron ion channel behaviors and computational efficiency is essential for simulating the action potentials of large-scale networks. However, existing spiking neuron models cannot achieve both high biological realism and computational efficiency, resulting in SNN constructed from a single type of neuron to make a compromise between these two attributes, thus reducing the SNN effectiveness in disease simulation and analysis. In this paper, we propose a dual-mode spiking neuron hardware design with an efficient reconfigurable architecture to achieve both biological realism and computational efficiency for diseases modeling. By exploiting the common arithmetic operators in Hodgkin-Huxley neuron and Adaptive Exponential (AdEx) neuron, our design can reuse the computational units including adder, multiplier, and CORDIC to efficiently realize these two neurons. An optimized pipeline design based on data flow dependency and Reconfigurable Fast-Convergence CORDIC is proposed to reduce overall computation latency, while a dynamic bit-width allocation strategy is employed to improve the implementation accuracy. FPGA implementation result shows that our design significantly improves computation latency and accuracy compared to previous neuron designs.