Revolutionary Analog Processing

ANNET Technology

Eliminating A/D and D/A conversions by processing data directly in analog form. Experience 50x higher speed, 5x lower power consumption, and 100x lower latency.

Revolutionary Performance

50x faster

Speed Improvement

5x lower

Power Consumption

100x lower

Latency Reduction

Significantly lower

Cost Efficiency

Fundamental Principles

Revolutionary Approach

Traditional digital systems require conversion overhead at each step: analog → digital → processing → analog. ANNET bypasses all conversions by processing signals continuously in analog form.

Universal Approximators

Second-generation analog neural networks are universal approximators that can approximate any continuous analog function with a single hidden-layer artificial neural network. This enables direct processing of complex analog signals.

Continuous analog function approximation
Single hidden-layer capability
Direct signal processing
No quantization errors

Continuous Time Processing

ANNET treats time as a continuous variable, eliminating the need for clocks and counters while enabling temporal integration similar to biological neural networks. This results in truly real-time processing capabilities.

Clock-free operation
Biological-inspired temporal integration
Continuous time variable
Natural parallelism

Architectural Components

Analog Memory (aMEM)

Stores weights, bias values, inputs, and outputs as analog voltages.

Key Features:

Higher information density per cell than digital memory
Direct analog voltage storage
Enables efficient FIR filters and moving averages
No conversion overhead

Technical Advantages

Information Density:

Higher per cell

Storage Type:

Analog voltages

Analog Compute Unit (ACU)

Performs operations directly on analog signals using weights and biases.

Key Features:

Summation, multiplication, and comparison operations
Pure analog circuit implementation
No digital logic elements required
Instantaneous computation

Technical Advantages

Operation Type:

Pure analog

Logic Elements:

Not required

Management Unit (MU)

RISC-V based microprocessor for parameter loading and system control.

Key Features:

Loads parameters into aMEM
Monitors analog signal propagation
Low power RISC-V architecture
Flexible control via Management Link

Technical Advantages

Architecture:

RISC-V based

Power Consumption:

Ultra-low

Digital Memory (dMEM)

Stores configuration settings and network structure parameters.

Key Features:

Network structure configuration
Layer count and topology data
Separates control from processing
Ensures system programmability

Technical Advantages

Function:

Configuration

Programmability:

Ensured

Real-World Applications

Marine Applications

Underwater Systems

Long-duration underwater missions with 10³x energy reduction compared to digital solutions.

Key Applications:

Active sonar echolocation with real-time object classification
Biological-inspired acoustic pattern analysis
Underwater monitoring systems

Space Systems

Satellite Technology

30% reduction in electronic mass and 60% reduction in coolant mass for satellites.

Key Applications:

Attitude control with 0.2 Nm torque resolution
Solar panel optimization with 10¹⁰x energy efficiency
Adaptive signal modulation for LEO constellations
In-memory RF beamforming for Ka-band communications

Automotive ADAS

Safety Systems

Millisecond reaction times for Advanced Driver-Assistance Systems.

Key Applications:

Real-time object detection and recognition
Enhanced radar sensor capabilities
LiDAR processing with 30% extended range
Real-time 3D mapping

Security & IoT

Surveillance

Low-power sensor nodes with instantaneous alert mechanisms.

Key Applications:

Industrial surveillance systems
IoT ecosystem monitoring
Edge computing applications
Continuous environmental monitoring

ONNX Compatibility & Workflow Tools

Seamless integration with existing AI workflows

ANNET is fully compatible with ONNX (Open Neural Network Exchange), enabling easy conversion of existing digital neural network models from PyTorch, TensorFlow, and other popular frameworks into ANNET-compatible analog structures.

Automated model conversion

Analog structure generation

Configuration file creation