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Deep Convolutional Neural Networks

On Resource-Efficient Inference using Trained Convolutional Neural Networks

We are pleased to release new results on approximation of deep Convolutional Neural Networks (CNN). We used Ristretto to approximate trained 32-bit floating point CNNs. The key results can be summarized as follows: 8-bit dynamic fixed point is enough to approximate three ImageNet networks. 32-bit multiplications can be replaced by bit-shifts for small networks. Traditionally, CNNs are trained in 32-bit or 64-bit floating point. Deep CNNs are resource intense both in terms of computati...
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Hardware-oriented Approximation of Convolutional Neural Networks

This extended abstract describes our Ristretto framework for CNN compression. Last week Philipp presented the Ristretto Poster at ICLR'16 in San Juan. Abstract High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption. One of the most important steps in accelerator development is hardware-oriented model app...
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Design Space Exploration of FPGA-Based Deep Convolutional Neural Networks

Abstract Deep Convolutional Neural Networks (DCNN) have proven to be very effective in many pattern recognition applications, such as image classification and speech recognition. Due to their computational complexity, DCNNs demand implementations that utilize custom hardware accelerators to meet performance and energy-efficiency constraints. In this paper, we propose an FPGA-based accelerator architecture which leverages all sources of parallelism in DCNNs. We develop analytical feasibility and...
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