机译:使用基于串行FFT的MFCC和28-NM CMOS中的二值化深度可分离CNN的510-NW唤醒关键字斑点芯片
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Neuchatel EPFL Dept Elect Engn CH-10027 Neuchatel Switzerland;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Southeast Univ Natl ASIC Ctr Sch Elect Sci & Engn Nanjing 210096 Peoples R China;
Binary neural network (NN); data reuse; depthwise separable convolution (DSC); keyword spotting (KWS); near-threshold voltage (NTV) design; serial fast Fourier transform (FFT);
机译:始终在线的3.8
机译:CNN具有深度可分离卷曲和组合核的额定预测
机译:基于CNN的偏压和深度可分离卷积,用于偏振SAR图像分类
机译:14.1在28nm CMOS上使用基于串行FFT的MFCC和二值化深度可分离卷积神经网络的510nW 0.41V低存储器低计算关键字发现芯片
机译:采用恒定电荷减法的5.8nW45ppm /°C片上CMOS唤醒定时器
机译:通过深度可分离的CNN型号有效分类肺部声音,具有融合STFT和MFCC功能