论文标题
带有系统级本地自动增益控制的尖峰耳蜗
Spiking Cochlea with System-level Local Automatic Gain Control
论文作者
论文摘要
由于晶体管不匹配和模型的复杂性，包括局部自动增益控制（AGC）电路在硅耳蜗中的设计变得具有挑战性。为了解决这个问题，我们提出了一种替代系统级算法，该算法通过测量单个通道的输出尖峰活动来实现硅尖峰耳蜗中的通道特异性AGC。通道的带通滤波器增益动态地调整为输入幅度，以使平均输出尖峰速率停留在定义的范围内。由于此AGC机制只需要计数和添加操作，因此可以在未来的设计中以低硬件成本实现它。我们评估了局部AGC算法对分类任务的影响，其中输入信号在32 dB输入范围内变化。在语音与噪声分类任务上测试了接收耳蜗尖峰特征的两种分类器类型。当启用AGC时，逻辑回归分类器平均提高了6％，准确性相对提高40.8％。深度神经网络分类器对AGC案例显示出类似的改进，并且从Logistic回归分类器中获得91％的最佳准确性，平均准确性更高。
Including local automatic gain control (AGC) circuitry into a silicon cochlea design has been challenging because of transistor mismatch and model complexity. To address this, we present an alternative system-level algorithm that implements channel-specific AGC in a silicon spiking cochlea by measuring the output spike activity of individual channels. The bandpass filter gain of a channel is adapted dynamically to the input amplitude so that the average output spike rate stays within a defined range. Because this AGC mechanism only needs counting and adding operations, it can be implemented at low hardware cost in a future design. We evaluate the impact of the local AGC algorithm on a classification task where the input signal varies over 32 dB input range. Two classifier types receiving cochlea spike features were tested on a speech versus noise classification task. The logistic regression classifier achieves an average of 6% improvement and 40.8% relative improvement in accuracy when the AGC is enabled. The deep neural network classifier shows a similar improvement for the AGC case and achieves a higher mean accuracy of 96% compared to the best accuracy of 91% from the logistic regression classifier.
