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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Network-driven design principles for neuromorphic systems.

Johannes Partzsch¹, Rene Schüffny¹

¹Chair for Highly Parallel VLSI Systems and Neuromorphic Circuits, Department of Electrical Engineering and Information Technology, Technische Universität Dresden Dresden, Germany.

Frontiers in Neuroscience

|November 6, 2015

Summary

This summary is machine-generated.

This study introduces a network-driven approach for designing neuromorphic systems, optimizing architecture based on connectivity models to reduce silicon area and enhance efficiency.

Keywords:

Rent's rule mapping quality neuromorphic architectures synaptic connectivity system design

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Area of Science:

Neuromorphic Engineering
Computer Architecture
Computational Neuroscience

Background:

Synaptic connectivity is a major resource constraint in neuromorphic systems.
Current architectures prioritize technical factors over connectivity model optimization.
This leads to underutilization of potential resource efficiencies.

Purpose of the Study:

To develop a network-driven methodology for neuromorphic architecture design.
To analyze the performance of existing architectures in emulating connectivity models.
To demonstrate deriving optimized neuromorphic architectures from specific connectivity models.

Main Methods:

Introducing a generalized description for synapse matrix architectures.
Incorporating shared circuit component usage for area reduction.
Developing methods to analyze and derive architectures based on connectivity models.

Main Results:

Demonstrated a step-by-step process for deriving neuromorphic architectures from connectivity models.
Showcased architectures adapted to connection density, ensuring faithful model reproduction.
Achieved significant reductions in total silicon area for neuromorphic systems.

Conclusions:

The network-driven approach enables more area-efficient neuromorphic system design.
This methodology allows verification of connectivity resource usability.
Optimized architectures lead to faithful emulation of connectivity models with reduced hardware footprint.