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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Generalised Analog LSTMs Recurrent Modules for Neural Computing.

Kazybek Adam¹, Kamilya Smagulova², Alex James³

¹Department of Electronics and Nanoengineering, Alto University, Espoo, Finland.

Frontiers in Computational Neuroscience

|October 15, 2021

Summary

This summary is machine-generated.

This study presents analog memristive hardware for Long short-term memory (LSTM) networks, enabling efficient intelligent processing. The novel implementation addresses challenges in recurrent neural network applications and time-series prediction.

Keywords:

analog LSTM crossbar general-purpose LSTM memristors neural networks

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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Published on: November 12, 2019

Area of Science:

Neuroscience and Artificial Intelligence
Neuromorphic Engineering
Computer Engineering

Background:

The human brain functions as a complex recurrent neural network, inspiring computational models.
Traditional neural network models often struggle with feedback mechanisms and gradient issues.
Long short-term memory (LSTM) networks offer solutions to gradient problems and handle sequential data effectively.

Purpose of the Study:

To design and implement analog memristive hardware for LSTM networks.
To explore the advantages of continuous-domain analog computing for intelligent processing.
To address the open research problem of creating efficient, miniaturized hardware for neural networks.

Main Methods:

Developed hybrid CMOS-memristor circuits utilizing hafnium-oxide based memristor crossbars.
Tested architectures and circuits using TSMC 0.18 μm CMOS technology.
Conducted extensive SPICE simulations (over 3,500 inference, 300 system-level) and Monte Carlo simulations for variability analysis.

Main Results:

Demonstrated the feasibility of analog memristive LSTM hardware for time-series prediction.
Evaluated system performance considering memristor variability and circuit non-idealities.
Achieved efficient and miniaturized implementation compared to digital-only solutions.

Conclusions:

Analog memristive LSTM hardware offers a promising pathway for efficient intelligent processing.
The proposed implementation effectively handles time-series prediction tasks.
Further research into hybrid CMOS-memristor circuits can advance neuromorphic computing.