Applied Brain Research
Competes in neuromorphic processor development
referenced
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Innatera
Innatera develops ultra-low-power neuromorphic processors that enable real-time, on-device intelligence for sensor applications.
Neuromorphic Processor Innovator with Limited Brand Recognition
Total score
64.6
Brand Control Score
Score band
Leaking
overall control level
Control gap
10.7
strongest minus weakest block
Weakest area
Build Memory
lowest control area
Weakest block
Create Demand
lowest block score
Competitors
10
mapped competitor set
Control block scores
Total
64.6
Leaking
Choose the Market
66.7
Build the Brand
62.3
Create Demand
59.3
Convert Demand
70.0
Control areas
Choose the Market
Know the Market
70.0
Read Demand
60.0
Make Choices
70.0
Build the Brand
Be Recognised
52.0
Own Meaning
62.0
Win Choice
73.0
Create Demand
Control Channels
60.0
Build Memory
48.0
Balance Growth
70.0
Convert Demand
Control the Journey
65.0
Remove Friction
70.0
Guide Choice
75.0
Weakest signals
Leak badges
Recognition Leak
Demand Creation Leak
Channel Fragility
Conversion Friction
Strategic Blur
Journey Blind Spot
Competitor set
Syntiant
Offers deep learning solutions for edge AI applications
referenced
Unconventional AI
Focuses on energy-efficient AI hardware
referenced
Tenstorrent
Develops AI hardware and software solutions
referenced
Kneron
Provides integrated edge AI solutions
referenced
Hailo
Competes in edge AI processing
profiled
BrainChip
Specializes in neuromorphic AI processors
referenced
Mythic
Specializes in analog AI inference chips
profiled
GreenWaves Technologies
Develops ultra-low-power AI processors
referenced
GrAI Matter Labs
Focuses on neuromorphic computing solutions
referenced
Profile summary
Manufacturing
Neuromorphic Processors
Netherlands
B2B
Software product
Product development teams in consumer electronics, IoT, industrial, and wearable sectors
Neuromorphic processors
Enabling real-time, on-device intelligence for sensor applications
Need for real-time processing in edge devices, Desire to improve energy efficiency, Requirement for on-device intelligence
Processing speed, Power efficiency, Integration ease with existing systems
Neuromorphic processor architecture, Spiking neural network technology, Proven real-world applications