Connectionist Quantization Functions
نویسندگان
چکیده
One of the main strengths of connectionist systems, also known as neural networks, is their massive parallelism. However, most neural networks are simulated on serial computers where the advantage of massive parallelism is lost. For large and real-world applications, parallel hardware implementations are therefore essential. Since a discretization or quantization of the neural network parameters is of great beneet for both analog and digital hardware implementations , they are the focus of study in this paper. In 1987 a successful weight discretization method was developed, which is exible and produces networks with few discretization levels and without signiicant loss of performance. However, recent studies have shown that the chosen quantization function is not optimal. In this paper, new quantization functions are introduced and evaluated for improving the performance of this exible weight discretization method.
منابع مشابه
Published in the Proceedings of the SIPAR Workshop on Parallel and Distributed Computing Gen eve Switzerland October Connectionist Quantization Functions
One of the main strengths of connectionist systems also known as neural networks is their massive parallelism However most neural networks are simulated on serial computers where the advantage of massive parallelism is lost For large and real world applications parallel hardware implementations are therefore essential Since a discretization or quantization of the neural network parameters is of...
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