Threshold Neuron

Threshold Neurons are a novel artificial neuron model inspired by the threshold mechanisms and the excitation-inhibition balance observed in biological neurons. This model is designed to enhance the computational efficiency of on-device Deep Neural Networks (DNNs), which is a significant challenge in mobile and edge computing. As tasks become increasingly complex and computational resources remain constrained, research has focused on compressing neural network structures and optimizing systems. However, limited attention has been given to optimizing the fundamental building blocks of neural networks: the neurons. Threshold Neurons offer greater efficiency than the traditional neuron paradigm by significantly reducing hardware implementation complexity. Extensive experiments validate the effectiveness of neural networks utilizing Threshold Neurons, achieving substantial power savings of 7.51x to 8.19x and area savings of 3.89x to 4.33x at the kernel level, with minimal loss in precision. Furthermore, FPGA-based implementations of these networks demonstrate 2.52x power savings and 1.75x speed enhancements at the system level.

Category: Artificial Intelligence
Subcategory: Deep Learning
Tags: Threshold Neuronon-device inferencecomputational efficiencypower savingsFPGA
AI Type: Deep Learning
Programming Languages: Not specified
Frameworks/Libraries: Not specified
Application Areas: Mobile and edge computing
Manufacturer Company: Not specified
Country: Not specified
Algorithms Used

Deep Neural Networks

Model Architecture

Threshold Neurons

Datasets Used

Not specified

Performance Metrics

Power savings, area savings, speed enhancements

Deployment Options

On-device

Cloud Based

No

On Premises

Yes

Features

Reduces hardware complexity, enhances efficiency

Enterprise

No

Hardware Requirements

FPGA

Supported Platforms

Not specified

Interoperability

Not specified

Security Features

Not specified

Compliance Standards

Not specified

Certifications

Not specified

Open Source

No

Source Code URL

http://Not specified

Documentation URL

http://Not specified

Community Support

Not specified

Contributors

Not specified

Training Data Size

Not specified

Inference Latency

Not specified

Energy Efficiency

Substantial power savings

Explainability Features

Not specified

Ethical Considerations

Not specified

Known Limitations

Not specified

Industry Verticals

Not specified

Use Cases

Efficient on-device inference

Customer Base

Not specified

Integration Options

Not specified

Scalability

Not specified

Support Options

Not specified

SLA

Not specified

User Interface

Not specified

Multi-Language Support

No

Localization

Not specified

Pricing Model

Not specified

Trial Availability

No

Partner Ecosystem

Not specified

Patent Information

Not specified

Regulatory Compliance

Not specified

Version

Not specified

Website URL

http://Not specified

Service Type

Not specified

Has API

No

API Details

Not specified

Business Model

Not specified

Price

0.00

Currency

Not specified

License Type

Not specified

Release Date

01/01/1970

Last Update Date

01/01/1970

Contact Email

Not specified

Contact Phone

Not specified

Social Media Links

http://Not specified

Other Features

Not specified

Published

Yes