Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep neural networks have been shown to outperform prior state-of-the-art solutions that often relied heavily on hand-engineered feature extraction techniques coupled with simple classification algorithms. In particular, deep max-pooling convolutional neural networks (MPCNN) have been shown to dominate on several popular public benchmarks. Unfortunately, the benefits of deep networks have yet to be exploited in embedded, resource-bound settings that have strict por and area budgets. GPUs have been shown to improve throughput and energy-efficiency over CPUs due to their parallel architecture. In a similar fashion, FPGAs can improve performance while allowing more fine control over implementation. In order to meet por, area, and latency constraints, it is necessary to develop network reduction strategies in addition to optimal mapping. This work looks at two specific reduction techniques including limited precision for both fixed-point and floating-point formats, and performing ight matrix truncation using singular value decomposition. An FPGA-based framework is also proposed and used to deploy the trained networks. To demonstrate, a handful of public computer vision datasets including MNIST, CIFAR-10, and SVHN are fully implemented on a low-por Xilinx Artix-7 FPGA. Experimental results show that all networks are able to achieve a classification throughput of 16 img/sec and consume less than 700 mW when running at 200 MHz. In addition, the reduced networks are able to, on average, reduce por and area utilization by 37% and 44%, respectively, while only incurring less than 0.20% decrease in accuracy.
['Adam Page', 'Tinoosh Mohsenin']
I am writing a research paper on a idea FPGA-Based Reduction Techniques for Efficient Deep Neural Network Deployment
[{"from": "human", "value": "I am writing a research paper on a idea FPGA-Based Reduction Techniques for Efficient Deep Neural Network Deployment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep neural networks have been shown to outperform prior state-of-the-art solutions that often relied heavily on hand-engineered feature extraction techniques coupled with simple classification algorithms. In particular, deep max-pooling convolutional neural networks (MPCNN) have been shown to dominate on several popular public benchmarks. Unfortunately, the benefits of deep networks have yet to be exploited in embedded, resource-bound settings that have strict por and area budgets. GPUs have been shown to improve throughput and energy-efficiency over CPUs due to their parallel architecture. In a similar fashion, FPGAs can improve performance while allowing more fine control over implementation. In order to meet por, area, and latency constraints, it is necessary to develop network reduction strategies in addition to optimal mapping. This work looks at two specific reduction techniques including limited precision for both fixed-point and floating-point formats, and performing ight matrix truncation using singular value decomposition. An FPGA-based framework is also proposed and used to deploy the trained networks. To demonstrate, a handful of public computer vision datasets including MNIST, CIFAR-10, and SVHN are fully implemented on a low-por Xilinx Artix-7 FPGA. Experimental results show that all networks are able to achieve a classification throughput of 16 img/sec and consume less than 700 mW when running at 200 MHz. In addition, the reduced networks are able to, on average, reduce por and area utilization by 37% and 44%, respectively, while only incurring less than 0.20% decrease in accuracy."}]