Publication

Connectionist Quantization Functions

1996
Conference Papers

Abstract

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 benefit 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 flexible and produces networks with few discretization levels and without significant 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 flexible weight discretization method.

Official source

https://infoscience.epfl.ch/entities/publication/cbf494e6-68aa-4dbf-9f7a-1c9d20d4e14e

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1996
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/cbf494e6-68aa-4dbf-9f7a-1c9d20d4e14e

About this result

Related concepts (32)

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Canonical quantization

In physics, canonical quantization is a procedure for quantizing a classical theory, while attempting to preserve the formal structure, such as symmetries, of the classical theory, to the greatest extent possible. Historically, this was not quite Werner Heisenberg's route to obtaining quantum mechanics, but Paul Dirac introduced it in his 1926 doctoral thesis, the "method of classical analogy" for quantization, and detailed it in his classic text Principles of Quantum Mechanics.

Related publications (1)

Connectionist Quantization Functions

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