Analog hardware implementation of spike-based delayed feedback reservoir computing system

Li, Jialing; Zhao, Chao; Hamedani, Kian; Yi, Yang

doi:10.1109/ijcnn.2017.7966288

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…In spiking neural networks, input is encoded into spikes and each spike is represented as a single binary bit. There are two types of encoding approaches [7,10,45,47,[69][70][71][72]. The first type of encoding approach is rate encoding.…”

Section: Recent Progress In Neuromorphic Computingmentioning

confidence: 99%

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Quantized Neural Networks and Neuromorphic Computing for Embedded Systems

Liu¹,

Yi²

2020

Intelligent System and Computing

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Deep learning techniques have made great success in areas such as computer vision, speech recognition and natural language processing. Those breakthroughs made by deep learning techniques are changing every aspect of our lives. However, deep learning techniques have not realized their full potential in embedded systems such as mobiles, vehicles etc. because the high performance of deep learning techniques comes at the cost of high computation resource and energy consumption. Therefore, it is very challenging to deploy deep learning models in embedded systems because such systems have very limited computation resources and power constraints. Extensive research on deploying deep learning techniques in embedded systems has been conducted and considerable progress has been made. In this book chapter, we are going to introduce two approaches. The first approach is model compression, which is one of the very popular approaches proposed in recent years. Another approach is neuromorphic computing, which is a novel computing system that mimicks the human brain.

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Section: Recent Progress In Neuromorphic Computingmentioning

confidence: 99%

“…In recent years, an increasing number of researchers have started to implement neuromorphic computing using analog integrated circuits [46,47,49,50,71,[73][74][75][76][77][78][79]. Compared to digital implementation, analog implementation of neuromorphic computing is more energy efficient.…”

Section: Recent Progress In Neuromorphic Computingmentioning

confidence: 99%

Quantized Neural Networks and Neuromorphic Computing for Embedded Systems

Liu¹,

Yi²

2020

Intelligent System and Computing

Self Cite

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“…Particularly, in analog reservoir computers, fabricating a fully analog encoding spike generator is of crucial importance both to speed the process up and to optimize the required energy and hardware costs. Examples of such generators have been proposed, mainly, for analog implementation of delayed feedback reservoirs [59,60].…”

Section: Rc On Analog Neuromorphic Microchipsmentioning

confidence: 99%

Neuromorphic Electronic Systems for Reservoir Computing

Hadaeghi¹

2019

Preprint

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This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been developed. Here, a review of these experimental studies is provided to illustrate the progress in this area and to address the technical challenges which arise from this specific hardware implementation. Moreover, to deal with challenges of computation on such unconventional substrates, several lines of potential solutions are presented based on advances in other computational approaches in machine learning.

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“…Energy efficiency is another motivation to use the spiking neurons. TrueNorth chip consumes only 70 milliWatts (mW) to run 1 million spiking neurons with 256 million synapses [13][14][15]. The energy efficiency of spiking neural networks (SNNs) makes them a suitable choice for hardware implementations of artificial neurons as well [16,17].…”

Section: Introductionmentioning

confidence: 99%

The Novel Applications of Deep Reservoir Computing in Cyber-Security and Wireless Communication

Hamedani¹,

Zhou²,

Bai³

et al. 2020

Intelligent System and Computing

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This chapter introduces the novel applications of deep reservoir computing (RC) systems in cyber-security and wireless communication. The RC systems are a new class of recurrent neural networks (RNNs). Traditional RNNs are very challenging to train due to vanishing/exploding gradients. However, the RC systems are easier to train and have shown similar or even better performances compared with traditional RNNs. It is very essential to study the spatio-temporal correlations in cyber-security and wireless communication domains. Therefore, RC models are good choices to explore the spatio-temporal correlations. In this chapter, we explore the applications and performance of delayed feedback reservoirs (DFRs), and echo state networks (ESNs) in the cyber-security of smart grids and symbol detection in MIMO-OFDM systems, respectively. DFRs and ESNs are two different types of RC models. We also introduce the spiking structure of DFRs as spiking artificial neural networks are more energy efficient and biologically plausible as well.

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Analog hardware implementation of spike-based delayed feedback reservoir computing system

Cited by 20 publications

References 22 publications

Quantized Neural Networks and Neuromorphic Computing for Embedded Systems

Quantized Neural Networks and Neuromorphic Computing for Embedded Systems

Neuromorphic Electronic Systems for Reservoir Computing

The Novel Applications of Deep Reservoir Computing in Cyber-Security and Wireless Communication

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