Oct 20, 2016 · Abstract:We quantify a source of ineffectual computations when processing the multiplications of the convolutional layers in Deep Neural ...
We propose Pragmatic (PRA), a massively data-parallel architecture that eliminates most of the ineffectual computations on-the-fly, improving performance and ...
We propose Pragmatic (PRA), a massively data-parallel architecture that eliminates most of the ineffectual computations on-the-fly, improving performance and ...
We quantify a source of ineffectual computations when processing the multiplica- tions of the convolutional layers in Deep Neural Networks (DNNs) and ...
Jul 21, 2022 · Abstract: We quantify a source of ineffectual computations when processing the multiplications of the convolutional layers in Deep Neural ...
This work proposes Pragmatic (PRA), a massively data-parallel architecture that eliminates most of the ineffectual computations on- the-fly, ...
Oct 20, 2016 · Abstract—We quantify a source of ineffectual computations when processing the multiplications of the convolutional layers in. Deep Neural ...
Oct 18, 2017 · ABSTRACT. Deep Neural Networks expose a high degree of parallelism, mak- ing them amenable to highly data parallel architectures. However,.
Deep Neural Networks expose a high degree of parallelism, making them amenable to highly data parallel architectures. However, data-parallel architectures ...
Lane Shared Bit-Pragmatic Deep Neural Network Computing ...
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Jul 8, 2020 · This brief proposes a lane shared bit-pragmatic architecture to address the synchronization induced performance bottleneck and hence further ...