Extended Data Fig. 4: Experimental MLPerf RNNT weight programming.
From: An analog-AI chip for energy-efficient speech recognition and transcription
Experimental correlation between target and programmed weights on chip-1 over 32 tiles for both (a) WP1 and (b) WP2. (c),(d) The corresponding probability distribution functions (PDF) of errors, expressed as percentage of the maximum weight, reveal high-yield chips with very few erroneous weights. (e) Table showing the analog yield, or the fraction of weights with programming error within 20% of the maximum weight. After integration of the PDFs in (c,d), the corresponding cumulative distribution functions (CDFs) are computed and the 1%-99% spread is collected, providing 2 data points (one for WP1 and one for WP2) for each tile. The plot shows the corresponding CDFs for each of the five chips used in RNNT experiments. To control the peripheral circuitry saturation, some tiles have weights mapped into a smaller conductance range (Max W equal to 80), leading to a different 1%-99% spread, e.g. the points with increased spread on chip-1 CDF in (e).
