DeepAI
Log In Sign Up

Integrate-and-Fire Neurons for Low-Powered Pattern Recognition

06/28/2021
by   Florian Bacho, et al.
0

Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the data and the decision is complex and/or the amount of data to transmit is large (e.g. in biologgers). Artificial Neural Networks (ANNs) can efficiently detect patterns in the input data which makes them suitable for decision making or compression of information for data transmission. However, ANNs require a substantial amount of energy which reduces the lifetime of battery-powered devices. Therefore, the use of Spiking Neural Networks can improve such systems by providing a way to efficiently process sensory data without being too energy-consuming. In this work, we introduce a low-powered neuron model called Integrate-and-Fire which exploits the charge and discharge properties of the capacitor. Using parallel and series RC circuits, we developed a trainable neuron model that can be expressed in a recurrent form. Finally, we trained its simulation with an artificially generated dataset of dog postures and implemented it as hardware that showed promising energetic properties. This paper is the full text of the research, presented at the 20th International Conference on Artificial Intelligence and Soft Computing Web System (ICAISC 2021)

READ FULL TEXT
03/31/2018

Hardware design of LIF with Latency neuron model with memristive STDP synapses

In this paper, the hardware implementation of a neuromorphic system is p...
05/19/2018

Reliable counting of weakly labeled concepts by a single spiking neuron model

Making an informed, correct and quick decision can be life-saving. It's ...
09/10/2018

Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

The spiking neural networks (SNNs), the 3rd generation of neural network...
08/23/2019

Spiking Neural Predictive Coding for Continual Learning from Data Streams

For energy-efficient computation in specialized neuromorphic hardware, w...
02/27/2016

Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing

Large-scale artificial neural networks have shown significant promise in...
11/09/2022

Spiking Neural Network Decision Feedback Equalization

In the past years, artificial neural networks (ANNs) have become the de-...
09/02/2021

Self-timed Reinforcement Learning using Tsetlin Machine

We present a hardware design for the learning datapath of the Tsetlin ma...