Edge Intelligence to Cloud Systems
Edge Intelligence to Cloud Systems
AI systems engineered with processors, accelerators, sensors, and neural networks can replace the traditional IT systems and adapt to your business
Vertically Integrated AI Products
Processors
Acclerators
Sensors
Data Connectors
Neural Networks
Simulation
AI Systems Engineering
AI is no longer a layer of software or application. The AI system architecture has evolved inside-out with the latest advances in semiconductor processors, low level computational libraries, and trained neural networks. Our systems engineering team can solve the most complex engineering problems for delivering your AI products. These hardware and software engineers are equipped with expertise to benchmark enterprise standards, performance, and security at all stages of the AI system life cycle.
- Validate Product Specifications
- Build Proof-Of-Concept and Pilot
- Modular and Adaptable Components
- Deep Learning Algorithms
- Adaptive Testing Framework
- Production Operationalization
AI Studio
ICURO made significant investments with artificial intelligence (AI) hardware in equipping the Silicon Valley AI systems lab in Santa Clara, California with enterprise grade sensors, semiconductor chip sets, edge computing processors, graphical processing units, system on chips, robots, and actuators.
ICURO’s AI Studio demonstrates our commitment to people growth initiatives in artificial intelligence. It offers a unique hands-on AI system infrastructure and industry use cases for both software and hardware engineers to become AI system specialists.
Team Up With Our AI Specialists
AI is no longer a layer of software or application. The AI system architecture has evolved inside-out with the latest advances in semiconductor processors, low level computational libraries, and trained neural networks. Our systems engineering team can solve the most complex engineering problems for delivering your AI products. These hardware and software engineers are equipped with expertise to benchmark enterprise standards, performance, and security at all stages of the AI system life cycle.
- Machine learning to define and execution of neural network training, and pruning and optimization techniques for inferencing.
- Sensor fusion includes sensor hardware and algorithms to fuse LiDAR data, camera images, IMU data using algorithms to perceive the environment and deliver action.
- Embedded processors that orchestrates and coordinates computations among accelerators.
- Accelerators to perform highly parallel operations required by AI and enables simultaneous computations.
- Edge computing architecture includes edge hardware and software stack for sensing and actuation along with reasonable power, communications and security.
- Mechatronics for the mechanical aspects of actuation, electronics, control units, and robotics.
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