A High-Accuracy and Energy-Efficient CORDIC Based Izhikevich Neuron With Error Suppression and Compensation
A High-Accuracy and Energy-Efficient CORDIC Based Izhikevich Neuron With Error Suppression and Compensation
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DOI：
10.1109/tbcas.2022.3191004
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发表时间：
2022-07
影响因子：
5.1
通讯作者：
Jipeng Wang;Zixuan Peng;Yi Zhan;Yujie Li;Guoyi Yu;Kwen-Siong Chong;Chao Wang
中科院分区：
文献类型：
--
作者：
Jipeng Wang;Zixuan Peng;Yi Zhan;Yujie Li;Guoyi Yu;Kwen-Siong Chong;Chao Wang
仿生神经元模型是用于脑科学探索和神经形态工程应用的类脑神经网络的关键构建模块。仿生神经元模型的高效硬件设计是实现类脑神经网络的挑战之一，因为模型精度、能耗和硬件成本的平衡非常具有挑战性。本文提出了一种基于 Izhikevich 神经元设计的高精度、高能效快速收敛坐标旋转数字计算机（FC-CORDIC）。为保证模型精度，提出了Izhikevich神经元的误差传播模型，进行系统误差分析并有效降低误差。提出参数调整误差补偿(PTEC)方法和位宽扩展误差抑制(BEES)方法来有效减少Izhikevich神经元设计的误差。此外，Izhikevich模型中采用FC-CORDIC代替传统CORDIC进行平方计算，去除了多余的CORDIC迭代，有效减少了累积误差和所需计算量，显着提高了精度和能量效率。还提出了FC-CORDIC的优化定点设计，以在保证精度的同时节省硬件开销。 FPGA 实现结果表明，在最先进的设计中，所提出的 Izhikevich 神经元设计可以在可接受的硬件开销下实现高精度和能效。
Bio-inspired neuron models are the key building blocks of brain-like neural networks for brain-science exploration and neuromorphic engineering applications. The efficient hardware design of bio-inspired neuron models is one of the challenges to implement brain-like neural networks, as the balancing of model accuracy, energy consumption and hardware cost is very challenging. This paper proposes a high-accuracy and energy-efficient Fast-Convergence COordinate Rotation DIgital Computer (FC-CORDIC) based Izhikevich neuron design. For ensuring the model accuracy, an error propagation model of the Izhikevich neuron is presented for systematic error analysis and effective error reduction. Parameter-Tuning Error Compensation (PTEC) method and Bitwidth-Extension Error Suppression (BEES) method are proposed to reduce the error of Izhikevich neuron design effectively. In addition, by utilizing the FC-CORDIC instead of conventional CORDIC for square calculation in the Izhikevich model, the redundant CORDIC iterations are removed and therefore, both the accumulated errors and required computation are effectively reduced, which significantly improve the accuracy and energy efficiency. An optimized fixed-point design of FC-CORDIC is also proposed to save hardware overhead while ensuring the accuracy. FPGA implementation results exhibit that the proposed Izhikevich neuron design can achieve high accuracy and energy efficiency with an acceptable hardware overhead, among the state-of-the-art designs.