Above are papers related to the following paper: [Paper_ID] Paper #1135122. 	[Title] Quicknet on MultiSpert: Fast Parallel Neural Network Training [Abstract] The MultiSpert parallel system is a straight-forward extension of the Spert workstation accelerator, which is predominantly used in speech recognition research at ICSI. In order to deliver high performance for Artificial Neural Network training without requiring changes to the user interfaces, the exisiting Quicknet ANN library was modified to run on MultiSpert. In this report, we present the algorithms used in the parallelization of the Quicknet code and analyse their communication and computation requirements. The resulting performance model yields a better understanding of system speed-ups and potential bottlenecks. Experimental results from actual training runs validate the model and demonstrate the achieved performance levels. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2702. 	[Title] In  Fast Non-Linear Dimension Reduction [Abstract] We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Experiments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also more than an order of magnitude faster to train. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #948147. 	[Title] A Fast Kohonen Net Implementation for [Abstract] We present an implementation of Kohonen Self-Organizing Feature Maps for the Spert-II vector microprocessor system. The implementation supports arbitrary neural map topologies and arbitrary neighborhood functions. For small networks, as used in real-world tasks, a single Spert-II board is measured to run Kohonen net classification at up to 208 million connections per second (MCPS). On a speech coding benchmark task, Spert-II performs on-line Kohonen net training at over 100 million connection updates per second (MCUPS). This represents almost a factor of 10 improvement compared to previously reported implementations. The asymptotic peak speed of the system is 213 MCPS and 213 MCUPS. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #72056. 	[Title] MBP on T0: mixing floating- and fixed-point formats in BP learning [Abstract] We examine the efficient implementation of back prop type algorithms on T0 [4], a vector processor with a fixed point engine, designed for neural network simulation. A matrix formulation of back prop, Matrix Back Prop [1], has been shown to be very efficient on some RISCs [2]. Using Matrix Back Prop, we achieve an asymptotically optimal performance on T0 (about 0.8 GOPS) for both forward and backward phases, which is not possible with the standard on-line method. Since high efficiency is futile if convergence is poor (due to the use of fixed point arithmetic), we use a mixture of fixed and floating point operations. The key observation is that the precision of fixed point is sufficient for good convergence, if the range is appropriately chosen. Though the most expensive computations are implemented in fixed point, we achieve a rate of convergence that is comparable to the floating point version. The time taken for conversion between fixed and floating point is also shown to be reasonable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #733534. 	[Title] SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training [Abstract] We report on our development of a high-performance system for neural network and other signal processing applications. We have designed and implemented a vector microprocessor and packaged it as an attached processor for a conventional workstation. We present performance comparisons with commercial workstations on neural network backpropagation training. The SPERT-II system demonstrates significant speedups over extensively hand optimization code running on the workstations. Please summarise all the given information.

Paper #1135122 has the title `Quicknet on MultiSpert: Fast Parallel Neural Network Training  
` and the abstract `The MultiSpert parallel system is a straight-forward extension of the Spert workstation accelerator, which is predominantly used in speech recognition research at ICSI. In order to deliver high performance for Artificial Neural Network training without requiring changes to the user interfaces, the exisiting Quicknet ANN library was modified to run on MultiSpert. In this report, we present the algorithms used in the parallelization of the Quicknet code and analyse their communication and computation requirements. The resulting performance model yields a better understanding of system speed-ups and potential bottlenecks. Experimental results from actual training runs validate the model and demonstrate the achieved performance levels. 
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Question: Which category should Paper #1135122 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1135122 has the title `Quicknet on MultiSpert: Fast Parallel Neural Network Training  
` and the abstract `The MultiSpert parallel system is a straight-forward extension of the Spert workstation accelerator, which is predominantly used in speech recognition research at ICSI. In order to deliver high performance for Artificial Neural Network training without requiring changes to the user interfaces, the exisiting Quicknet ANN library was modified to run on MultiSpert. In this report, we present the algorithms used in the parallelization of the Quicknet code and analyse their communication and computation requirements. The resulting performance model yields a better understanding of system speed-ups and potential bottlenecks. Experimental results from actual training runs validate the model and demonstrate the achieved performance levels. 
`.
Question: Which category should Paper #1135122 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks