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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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AlexNet Forward Path Implementation

In this post I'll talk in detail about the forward path implementation of the famous AlexNet. This Convolutional Neural Network (CNN) by Krizhevsky and Hinton has won the ILSVR 2012 competition with a remarkable margin. If you are starting to work with CNNs or Deep Learning in general, this post will give you a head start. You can find a straight forward implementation of the CNN's forward path on our Github site. Feel free to download it and classify arbitrary images. When I was looking for ...
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Primary Studies on the real.iZ VS-1000 3D Vision System

This document is the primary studies on the real.iZ VS-1000 3D Vision System for fruit detection. Please go to manuals and odos imaging for more information and always regard the manuals as your prioritized reference. The contents are listed as follows: structure and operating principles features indoor experiment outdoor experiments range photos processing portable solutions future challenges help seeking 1.structure and operating principles The yellow outlined pa...
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Data Parser in Caffe

This document briefly explains how to use the parser of the Caffe code base in order to read and parse the specifications of neural networks. Caffe's text file format for specifying models uses Google Protocol Buffer format. The code that actually reads a model can be found in src/caffe/util/io.cpp: bool ReadProtoFromTextFile(const char* filename, Message* proto) { int fd = open(filename, O_RDONLY); CHECK_NE(fd, -1) << "File not found: " << filename; FileInputStream* inpu...
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