Ponte: Represent Totally Binary Neural Network Toward Efficiency

Ponte: Represent Totally Binary Neural Network Toward Efficiency

Blog Article

In the quest for computational efficiency, binary neural networks (BNNs) have emerged as a promising paradigm, offering significant reductions in memory footprint and computational latency.In traditional BNN implementation, the first and last layers are typically full-precision, which causes higher logic usage in field-programmable gate array (FPGA) implementation.To solve these issues, we introduce a novel approach named Ponte (Represent Totally Binary Neural Network Toward Chlorella Efficiency) that extends the binarization process to the first and last layers of BNNs.We challenge the convention by proposing a fully binary layer replacement that mitigates the computational overhead without compromising accuracy.Our method leverages a unique encoding technique, Ponte::encoding, and a channel duplication strategy, Ponte::dispatch, and Ponte::sharing, to address the non-linearity and capacity constraints posed by binary layers.

Surprisingly, all of them are back-propagation-supported, which allows our work to be implemented in the last layer through extensive experimentation on benchmark datasets, including CIFAR-10 and ImageNet.We demonstrate that Ponte not only preserves the integrity of input Course a pied - Femme - Vetements - Chandail - Long sleeves brosse data but also enhances the representational capacity of BNNs.The proposed architecture achieves comparable, if not superior, performance metrics while significantly reducing the computational demands, thereby marking a step forward in the practical deployment of BNNs in resource-constrained environments.