论文标题
思维阅读：基于脑电图的人类意图识别的超低功率光子加速器
MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-based Human Intention Recognition
论文作者
论文摘要
头皮录制的脑电图（EEG）基于脑部计算机界面（BCI）系统可以极大地改善患有运动障碍的人的生活质量。创建了由多个卷积，LSTM和完全连接的层组成的深层神经网络来解码EEG信号，以最大程度地提高人类意图识别精度。但是，在处理实时意图识别时，先前的FPGA，ASIC，RERAM和光子加速器无法保持足够的电池寿命。在本文中，我们提出了一个超低功能光子加速器，即思维读物，仅通过低位宽度和转移操作来识别人类意图。与先前的神经网络加速器相比，要保持实时处理吞吐量，MindReading将功耗降低了62.7 \％，并将每瓦的吞吐量提高168 \％。
A scalp-recording electroencephalography (EEG)-based brain-computer interface (BCI) system can greatly improve the quality of life for people who suffer from motor disabilities. Deep neural networks consisting of multiple convolutional, LSTM and fully-connected layers are created to decode EEG signals to maximize the human intention recognition accuracy. However, prior FPGA, ASIC, ReRAM and photonic accelerators cannot maintain sufficient battery lifetime when processing real-time intention recognition. In this paper, we propose an ultra-low-power photonic accelerator, MindReading, for human intention recognition by only low bit-width addition and shift operations. Compared to prior neural network accelerators, to maintain the real-time processing throughput, MindReading reduces the power consumption by 62.7\% and improves the throughput per Watt by 168\%.
