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Automated design of error-resilient and hardware-efficient deep neural networks
It is thus desirable to exploit optimization potential for error resilience and efficiency also at the algorithmic side, e. g., by optimizing the architecture of the DNN.
Bosch Deep Learning Hardware Benchmark
The widespread use of Deep Learning (DL) applications in science and industry has created a large demand for efficient inference systems.
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