 Название: Neuro-inspired Information Processing
Автор: Alain Cappy
Серия: Electronics Engineering Series
Издательство: Wiley-ISTE
Год: 2020
Страниц: 230
Язык: английский
Формат: pdf (true)
Размер: 11.5 MB
With the end of Moore's law and the emergence of new application needs such as those of the Internet of Things (IoT) or Artificial Intelligence (AI), neuro-inspired, or neuromorphic, information processing is attracting more and more attention from the scientific community. Its principle is to emulate in a simplified way the formidable machine to process information which is the brain, with neurons and artificial synapses organized in network. These networks can be software – and therefore implemented in the form of a computer program – but also hardware and produced by nanoelectronic circuits. The "material" path allows very low energy consumption, and the possibility of faithfully reproducing the shape and dynamics of the action potentials of living neurons (biomimetic approach) or even being up to a thousand times faster (high frequency approach). This path is promising and welcomed by the major manufacturers of nanoelectronics, as circuits can now today integrate several million neurons and artificial synapses.
In order for the co-integration of “von Neumann/CMOS and artificial neural networks (ANN)” to be rapid and straight-forward, technological compatibility is essential. With the CMOS technology set to continue to dominate for many years to come, the fabrication of ANNs using CMOS as the main fabrication process would appear essential. It is conceivable that in the near future, mixed processing systems associating standard processors and neuromorphic coprocessors will emerge.
Contents:
Acknowledgments . . . . . . . ix
Introduction . . . . . . . . xi
Chapter 1. Information Processing . . .. . . . 1
1.1. Background . . . . . . . . . . 1
1.1.1. Encoding . . . . . . . . . . 2
1.1.2. Memorization . . . . . . . . 4
1.2. Information processing machines . . . . . 5
1.3. Information and energy . . . . . . . 16
1.4. Technologies of the future . . . . . 26
1.5. Microprocessors and the brain . . . . 40
Chapter 2. Information Processing in the Living . . . . . . 47
Chapter 3. Neurons and Synapses . . . . . . . . . . 67
Chapter 4. Artificial Neural Networks . . . . . . . 129
4.1. Software neural networks . . . . . . . . . . . 130
4.1.1. Neuron and synapse models. . . . . . . . 130
4.1.2. Artificial Neural Networks . . . . . . . 133
4.1.3. Learning . . . . . . . . . . . . . . . . 140
4.1.4. Conclusion . . . . . . . . . . . . . . . 147
4.2. Hardware neural networks . . . . . . . . . 148
4.2.1. Comparison of the physics of biological systems and semiconductors . . . 149
4.2.2. Circuits simulating the neuron . . . . . 154
4.2.3. Circuits simulating the synapse . . . . 189
4.2.4. Circuits for learning . . . . . . . . . 198
4.2.5. Examples of hardware neural networks . . 201
4.3. Conclusion . . . . . 210
References . . . . . . 211
Index . . . . . . 219
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