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2c exhibits how durations from odd columns in the highest tile may be merged along with durations from even columns from The underside tile. This configuration was utilized about the RNNT Dec chip (Extended Data Fig. 7c).

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To encode the MLPerf RNNT weights, we used 5 chips. Iterative excess weight programming enabled accurate tuning with the conductances to match the target weights. Heat maps correlating the concentrate on and also the calculated chip-1 weights on Each and every of your 32 tiles are shown for WP1 and WP2 in Prolonged Data Fig. 4a,b. The corresponding error for every tile, expressed as the portion of the most bodyweight, is revealed in Extended Knowledge Fig. 4c,d for WP1 and WP2. To match the weight programming during the 5 chips useful for the RNNT experiment, we calculated the CDF on the basis of the information demonstrated in Prolonged Information Fig.

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After the analog on-chip MAC, the final results are denormalized and the usual calibration was used. For every other layer (Prolonged Data Fig. 6c) while in the RNNT, the inputs had been applied straight as tile activations plus the MAC was calibrated with the usual affine coefficients. All affine coefficients are calculated by evaluating experimental and anticipated SW MAC applying 2,000 enter frames from the education dataset for every Enc–Dec layer. Info were being linearly fitted to acquire the slope and offset coefficients.

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