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Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing
2022
Frontiers in Neuroscience
With recent advances in the field of artificial intelligence (AI) such as binarized neural networks (BNNs), a wide variety of vision applications with energy-optimized implementations have become possible at the edge. Such networks have the first layer implemented with high precision, which poses a challenge in deploying a uniform hardware mapping for the network implementation. Stochastic computing can allow conversion of such high-precision computations to a sequence of binarized operations
doi:10.3389/fnins.2021.781786
pmid:35069101
pmcid:PMC8766965
fatcat:7xpaoytyg5ftpa4ng3jau3dkyi