2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)最新文献

英文中文

A 2.17mW Acoustic DSP Processor with CNN-FFT Accelerators for Intelligent Hearing Aided Devices 用于智能助听器的2.17mW声学DSP处理器与CNN-FFT加速器

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

Pub Date : 1900-01-01 DOI: 10.1109/AICAS.2019.8771631

Yu-Chi Lee, T. Chi, Chia-Hsiang Yang

This paper proposes an acoustic DSP processor with a neural network core for speech enhancement. Accelerators for convolutional neural network (CNN) and fast Fourier transform (FFT) are embedded. The CNN-based speech enhancement algorithm takes the speech signals spectrogram as the model’s input, and predicts the desired mask of speech to enhance speech intelligibility after passing through the CNN model. An array of multiply-accumulator (MAC) and coordinate rotation digital computer (CORDIC) engines are deployed to efficiently compute linear and nonlinear functions. Hardware sharing is applied to reduce hardware area by leveraging the high similarity between CNN and FFT computations. The proposed DSP processor chip is fabricated in a 40-nm CMOS technology with a core area of 4.3 mm2. The chip’s power dissipation is 2.17 mW at an operating frequency of 5 MHz. The CNN accelerator supports both convolutional and fully-connected layers and achieves an energy efficiency of 1200-to-2180 GOPS/W, despite the flexibility for FFT. The speech intelligibility can be enhanced by up to 41% under low SNR conditions.

提出了一种以神经网络为核心的声学DSP处理器，用于语音增强。嵌入了卷积神经网络(CNN)和快速傅里叶变换(FFT)的加速器。基于CNN的语音增强算法以语音信号谱图作为模型的输入，通过CNN模型预测语音的期望掩码，增强语音的可理解度。采用一组乘累加器(MAC)和坐标旋转数字计算机(CORDIC)引擎来高效地计算线性和非线性函数。利用CNN和FFT计算的高相似性，采用硬件共享来减少硬件面积。所提出的DSP处理器芯片采用40纳米CMOS技术制造，核心面积为4.3 mm2。该芯片在5mhz工作频率下的功耗为2.17 mW。CNN加速器支持卷积层和全连接层，尽管FFT具有灵活性，但能量效率达到1200至2180 GOPS/W。在低信噪比条件下，语音清晰度可提高41%。

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