19,626 research outputs found
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
State-of-the-art convolutional neural networks are enormously costly in both
compute and memory, demanding massively parallel GPUs for execution. Such
networks strain the computational capabilities and energy available to embedded
and mobile processing platforms, restricting their use in many important
applications. In this paper, we push the boundaries of hardware-effective CNN
design by proposing BCNN with Separable Filters (BCNNw/SF), which applies
Singular Value Decomposition (SVD) on BCNN kernels to further reduce
computational and storage complexity. To enable its implementation, we provide
a closed form of the gradient over SVD to calculate the exact gradient with
respect to every binarized weight in backward propagation. We verify BCNNw/SF
on the MNIST, CIFAR-10, and SVHN datasets, and implement an accelerator for
CIFAR-10 on FPGA hardware. Our BCNNw/SF accelerator realizes memory savings of
17% and execution time reduction of 31.3% compared to BCNN with only minor
accuracy sacrifices.Comment: 9 pages, 6 figures, accepted for Embedded Vision Workshop (CVPRW
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
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