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Abstract: Residual networks (ResNets) employ skip connections in their networks --reusing activations from previous layers -- to improve training convergence,but these skip connections create challenges for hardware implementations ofResNets. The hardware must either wait for skip connections to be processedbefore processing more incoming data or buffer them elsewhere. Without skipconnections, ResNets would be more hardware-efficient. Thus, we present theteacher-student learning method to gradually prune away all of a ResNet's skipconnections, constructing a network we call NonResNet. We show that whenimplemented for FPGAs, NonResNet decreases ResNet's BRAM utilization by 9% andLUT utilization by 3% and increases throughput by 5%.
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