Co-founder & CEO
Profile summary: PhD in Electrical Engineering - Microelectronics at Delft University of Technology
I
Innatera
Innatera develops ultra-low-power neuromorphic processors based on a proprietary analog-mixed signal computing architecture. These processors utilize spiking neural networks to enable high-performance pattern recognition directly at the sensor edge. The technology delivers cognition performance with ultra-low power consumption and short response latency for power-limited applications.
- Artificial Intelligence
- Hardware
- Internet of Things
- Semiconductor
Updated 14 days ago
Funding
Raised to date
$21MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 1 round
DEE+4$21M · Series A
Announced Mar 2024
Disclosed cumulative: $21M
RoundDateAmountValuationInvestors
Founders
Product
Problem
Many sensor-based applications require real-time pattern recognition but are constrained by power limitations and latency requirements, making traditional processing methods inadequate. Existing solutions often struggle to balance computational demands with energy efficiency, hindering the deployment of always-on cognitive processing in edge devices.
Solution
Innatera has developed neuromorphic processors that utilize spiking neural networks (SNNs) to enable real-time pattern recognition in sensor data while consuming less than 1mW of power. These processors mimic the brain's mechanisms for processing sensory data, leveraging a proprietary analog-mixed signal computing architecture. By using SNNs, Innatera's processors achieve high-performance, always-on pattern recognition capabilities for power-limited and latency-critical applications. The technology enables rapid recognition of patterns in sensor data and complex signal processing.
Target Audience
The primary target audience includes developers and manufacturers of power-sensitive and latency-critical applications that rely on sensor data, such as those in IoT, wearables, and embedded systems.
Features
- Ultra-low-power consumption (less than 1mW) for always-on cognitive processing
- Spiking Neural Network (SNN) architecture for efficient pattern recognition
- Proprietary analog-mixed signal computing architecture
- Real-time processing of sensor data with minimal latency
- Event-based neural networks that mimic brain processing mechanisms
- Temporal processing capabilities for identifying patterns in time-series data