Hardware Implementations of Spiking Neural Networks

Zheng, Nan; Mazumder, Pinaki

doi:10.1002/9781119507369.ch5

Cited by 2 publications

(1 citation statement)

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“…This field is fully developed, and one considerable number of treatises, references, and reviews on their fundamentals or about new frontiers of the research can be consulted. For example, it is possible to mention numerous references on the spiking neural networks area, including [36,50,51], only to cite just a few, or their implementation exploiting hardware techniques [52][53][54][55] or photonic procedure based [56] methods. The digital achievement of neurons in programmable devices is more than feasible, especially on FPGAs or fast processors.…”

Section: Summation Neuron Boardmentioning

confidence: 99%

Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons

Guerrero-Rodríguez

Cifredo-Chacon

Sánchez

et al. 2023

Sensors

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Pyroelectric infrared sensors (PIR) are widely used as infrared (IR) detectors due to their basic implementation, low cost, low power, and performance. Combined with a Fresnel lens, they can be used as a binary detector in applications of presence and motion control. Furthermore, due to their features, they can be used in autonomous intelligent devices or included in robotics applications or sensor networks. In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to the leaky integrate and fire model is implemented to be able to generate spiking rates proportional to the IR stimuli received at a PIR sensor. (2) An embedded postsynaptic neuron where a spiking neural network matrix together with an algorithm based on digital processing techniques is introduced. This structure allows connecting a set of sensors to the post-synaptic circuit emulating an optic nerve. As a case study, the entire neural processing approach presented in this paper is applied to optical flow detection considering a four-PIR array as input. The results validate both the spiking approach for an analog sensor presented and the ability to retrieve the analog information sent as spike trains in a simulated optic nerve.

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