Effects of multiplier output offsets on on-chip learning for analog neuro-chips

摘要

The effects of multiplier output offsets on on-chip learning are analyzed. Offsets on signal feed-forward multipliers are equivalent to sigmoid biases and do not degrade learning performance. Offsets on multipliers for sigmoid derivatives cause static errors at the outputs, which may be overcome by proper choices of target values. Offsets on weight-adjustment multipliers cause not only output static errors but also weight drifts, which are hard to compensate. Therefore weight-adjustment multipliers are most critical for analog neuro-chips with on-chip learning capability. Simulation results agree well with analytic calculations.