Above are papers related to the following paper: [Paper_ID] Paper #367312. 	[Title] A Performance Analysis of CNS-1 on Sparse Connectionist Networks [Abstract] This report deals with the efficient mapping of sparse neural networks on CNS-1. We develop parallel vector code for an idealized sparse network and determine its performance under three memory systems. We use the code to evaluate the memory systems (one of which will be implemented in the prototype), and to pinpoint bottlenecks in the current CNS-1 design. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #746058. 	[Title] All-to-all Broadcast on the CNS-1 [Abstract] This study deals with the all-to-all broadcast on the CNS-1. We determine a lower bound for the run time and present an algorithm meeting this bound. Since this study points out a bottleneck in the network interface, we also analyze the performance of alternative interface designs. Our analyses are based on a run time model of the network. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119211. 	[Title] A Supercomputer for Neural Computation [Abstract] The requirement to train large neural networks quickly has prompted the design of a new massively parallel supercomputer using custom VLSI. This design features 128 processing nodes, communicating over a mesh network connected directly to the processor chip. Studies show peak performance in the range of 160 billion arithmetic operations per second. This paper presents the case for custom hardware that combines neural network-specific features with a general programmable machine architecture, and briefly describes the design in progress. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #739280. 	[Title] A Performance Analysis of the CNS-1 on Large, Dense Backpropagation Networks Connectionist Network Supercomputer [Abstract] We determine in this study the sustained performance of the CNS-1 during training and evaluation of large multilayered feedforward neural networks. Using a sophisticated coding, the 128-node machine would achieve up to 111 Giga connections per second (GCPS) and 22 Giga connection updates per second (GCUPS). During recall the machine would archieve 87% of the peak multiply-accumulate performance. The training of large nets is less efficient than the recall but only by a factor of 1.5 to 2. The benchmark is parallelized and the machine code is optimized before analyzing the performance. Starting from an optimal parallel algorithm, CNS specific optimizations still reduce the run time by a factor of 4 for recall and by a factor of 3 for training. Our analysis also yields some strategies for code optimization. The CNS-1 is still in design, and therefore we have to model the run time behavior of the memory system and the interconnection network. This gives us the option of changing some parameters of the CNS-1 system in order to analyze their performance impact. Please summarise all the given information.

Paper #367312 has the title `A Performance Analysis of CNS-1 on Sparse Connectionist Networks  
` and the abstract `This report deals with the efficient mapping of sparse neural networks on CNS-1. We develop parallel vector code for an idealized sparse network and determine its performance under three memory systems. We use the code to evaluate the memory systems (one of which will be implemented in the prototype), and to pinpoint bottlenecks in the current CNS-1 design. 
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Question: Which category should Paper #367312 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #367312 has the title `A Performance Analysis of CNS-1 on Sparse Connectionist Networks  
` and the abstract `This report deals with the efficient mapping of sparse neural networks on CNS-1. We develop parallel vector code for an idealized sparse network and determine its performance under three memory systems. We use the code to evaluate the memory systems (one of which will be implemented in the prototype), and to pinpoint bottlenecks in the current CNS-1 design. 
`.
Question: Which category should Paper #367312 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