7 Ways Twitter Destroyed My Puma Without Me Noticing
Therefore, for YodaNN, we added clock gating for 11/12 input bits when binary layers are evaluated. Batch Normalization: This operation performs biasing of an input value in BNNs. Maxpooling: In a BNN, this operation is an OR operation on a pooling window of layer outputs. Therefore, batch normalization in Tulip is implemented using the comparison operation. In additon, a "ready" signal is generated through the "Ready generator" and sent to the control logic of the ADC, which controls the switches in the DAC array and sends the "delay signal" to the comparator before the next comparison is made. After the delay time ends, the first comparison is made directly between the voltages at the two inputs of the comparator. However, the Tulip-PE, is capable of preserving the data locality and can perform the comparison and max-pooling operations internally, without the need to move the data to other modules, which saves additional energy. However, these architectures do not support integer layers and are therefore not suitable for comparison. The TULIP architecture outperforms YodaNN in energy efficiency by about 3X for the convolution layers
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