uBrain: a unary brain computer interface
uBrain: a unary brain computer interface
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DOI：
10.1145/3470496.3527401
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Di Wu-;Jingjie Li;Zhewen Pan;Younghyun Kim
中科院分区：
文献类型：
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作者：
Di Wu-;Jingjie Li;Zhewen Pan;Younghyun Kim
脑机接口（BCI）被广泛用于通过具有丰富时空动态的脑信号（如脑电图（EEG））来增强人类的感知。近年来，BCI算法正在从经典的特征工程转向新兴的深度神经网络（DNN），从而能够以更高的精度识别时空动态。然而，现有的BCI架构没有利用这种动态来提高硬件效率。在这项工作中，我们提出了uBrain，这是一种用于DNN模型的一元计算BCI架构，具有级联卷积和递归神经网络，以实现高任务能力和硬件效率。uBrain共同设计算法和硬件：DNN架构和硬件架构分别通过定制的一元运算和感知后的即时信号处理进行优化。实验表明，uBrain在精度损失可以忽略不计的情况下，在片上功耗效率上分别超过CPU、脉动阵列和随机计算基准9.0倍、6.2倍和2.0倍。
Brain computer interfaces (BCIs) have been widely adopted to enhance human perception via brain signals with abundant spatial-temporal dynamics, such as electroencephalogram (EEG). In recent years, BCI algorithms are moving from classical feature engineering to emerging deep neural networks (DNNs), allowing to identify the spatial-temporal dynamics with improved accuracy. However, existing BCI architectures are not leveraging such dynamics for hardware efficiency. In this work, we present uBrain, a unary computing BCI architecture for DNN models with cascaded convolutional and recurrent neural networks to achieve high task capability and hardware efficiency. uBrain co-designs the algorithm and hardware: the DNN architecture and the hardware architecture are optimized with customized unary operations and immediate signal processing after sensing, respectively. Experiments show that uBrain, with negligible accuracy loss, surpasses the CPU, systolic array and stochastic computing baselines in on-chip power efficiency by 9.0×, 6.2× and 2.0×.