Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation

Anal Prakash Sharma;K. Akhilesh Rao;Laxmeesha Somappa
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Abstract

This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have gained prominence due to low on-chip memory requirements for binary classification. This work focuses on optimizing hardware performance and associated trade-offs from two fronts, namely, (a) implementation of the Neural Tree (NT) classifier using model compression techniques and (b) design of feature extraction engine (FEE) using FIR filters and time-division multiplexed hardware optimizations. We provide insights into how model compression techniques of Neural Networks like weight pruning and weight sharing can be exploited to reduce the memory requirement of Neural Tree inference hardware. Both these techniques effectively reduce non-zero weights and therefore help to reduce memory requirements. We also detail the choice of feature extraction engine (FEE) hardware to extract temporal and spectral features and the relevant area-power-attenuation trade-offs for spectral feature extraction. The end-to-end hardware comprising the FEE, the Neural Tree classifier and serial peripherals are tested on a Zynq-7000 series SoC using pre-recorded patient data. The SoC-based evaluation platform allows rapid testing of various model optimizations on hardware using AXI protocol. The entire system, trained on data from the CHB-MIT scalp EEG database, achieved a sensitivity of 95.7% and a specificity of 94.3%, with an on-chip memory of 0.59 kB. Implementing the design in a 65nm CMOS process resulted in a worst-case power of 174 $\mu$W and an area of 0.16 mm${}^{2}$. These findings along with the optimizations mark significant progress toward energy-efficient, scalable neuromodulation systems capable of real-time neurological disorder prediction.
用于片上闭环神经调节的 16 通道神经树分类器的硬件优化与实现。
这项工作介绍了用于植入式神经调节系统芯片(soc)的片上机器学习(ML)分类器的发展,旨在检测闭环神经调节应用的癫痫发作。基于树的分类器由于对二进制分类的片上内存要求低而获得了突出的地位。这项工作侧重于从两个方面优化硬件性能和相关权衡,即(a)使用模型压缩技术实现神经树(NT)分类器和(b)使用FIR滤波器和时分多路复用硬件优化设计特征提取引擎(FEE)。我们提供了如何利用神经网络的模型压缩技术(如权值修剪和权值共享)来减少神经树推理硬件的内存需求的见解。这两种技术都有效地减少了非零权重,因此有助于减少内存需求。我们还详细介绍了用于提取时间和光谱特征的特征提取引擎(FEE)硬件的选择,以及用于提取光谱特征的相关面积-功率衰减权衡。端到端硬件包括FEE、神经树分类器和串行外设,使用预先记录的患者数据在Zynq-7000系列SoC上进行测试。基于soc的评估平台允许使用AXI协议在硬件上快速测试各种模型优化。整个系统基于CHB-MIT头皮EEG数据库数据进行训练,灵敏度为95.7%,特异性为94.3%,片上内存为0.59 kB。在65nm CMOS工艺中实现该设计的最坏情况功率为174 μW,面积为0.16 mm2。这些发现和优化标志着节能、可扩展的神经调节系统取得了重大进展,能够实时预测神经系统疾病。
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