Extended Data Fig. 8: Experimental MLPerf RNNT MAC.
From: An analog-AI chip for energy-efficient speech recognition and transcription
(a) Error histogram and (b) correlation between experimental and target MAC are shown for every chip used during the RNNT inference experiment. On each correlation, white dotted lines highlight the main region of interest, since MACs are followed by sigmoid, tanh or ReLU which naturally filter out portions of MAC. The spread, σ, is calculated only within the highlighted Regions Of Interest (ROI). Data from both original Enc-LSTM0 and weight-expanded Enc-LSTM0 are reported, showing a better sigma for the weight expanded case. Enc-LSTM2, Enc-LSTM3, and Enc-LSTM4 show larger spread due to partial (Enc-LSTM2) or no (Enc-LSTM3, Enc-LSTM4) application of Asymmetry Balance. In addition, Enc-LSTM2 MAC is calculated on larger (3072 instead of 2048) inputs. Finally, decoder layers show larger σ, maybe caused by higher capacitor/Output Landing Pad saturation effects, which however have little impact on the overall WER, as revealed by the accuracy results in the main paper (Fig. 5a,b).
