基于结构可塑性的储层网络，实现低功耗边缘动作识别
Reservoir Network with Structural Plasticity for Human Activity Recognition
- 引入结构可塑性，支持芯片上在线自适应调整网络结构
- 在人体动作识别上达95.95%准确率，假肢手指控制达85.24%
- 65nm芯片实测每秒处理6万样本，仅耗电47.7mW
边缘设备的普及催生了对原生处理时序数据的类脑芯片的需求。本文提出一种面向边缘设备的类脑芯片，基于储层网络（ESN）设计，支持局部芯片上的结构可塑性与突触可塑性学习机制。该系统通过动态调整网络结构与稀疏度，实现持续学习与稳定性能。在真实时序数据集上验证了其抗噪能力与多种数据传输拓扑下的表现。实验结果表明，在人体动作识别任务中平均准确率达95.95%，在假肢手指控制任务中达85.24%。芯片在65nm IBM工艺下实现每秒6×10⁴样本处理速度，功耗仅为47.7mW。
原文摘要 · Abstract (English)
The unprecedented dissemination of edge devices is accompanied by a growing demand for neuromorphic chips that can process time-series data natively without cloud support. Echo state network (ESN) is a class of recurrent neural networks that can be used to identify unique patterns in time-series data and predict future events. It is known for minimal computing resource requirements and fast training, owing to the use of linear optimization solely at the readout stage. In this work, a custom-design neuromorphic chip based on ESN targeting edge devices is proposed. The proposed system supports various learning mechanisms, including structural plasticity and synaptic plasticity, locally on-chip. This provides the network with an additional degree of freedom to continuously learn, adapt, and alter its structure and sparsity level, ensuring high performance and continuous stability. We demonstrate the performance of the proposed system as well as its robustness to noise against real-world time-series datasets while considering various topologies of data movement. An average accuracy of 95.95% and 85.24% are achieved on human activity recognition and prosthetic finger control, respectively. We also illustrate that the proposed system offers a throughput of 6x10^4 samples/sec with a power consumption of 47.7mW on a 65nm IBM process.
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