Great to see Coretec through the merger with Core Optics at close will officially be partnered to some degree with all three companies listed here
"Samsung is the world’s second-largest maker of image sensors. However, its market share of 29 percent is paltry in front of the industry leader Sony, which captured 54 percent of the market last year. The Korean firm is rushing to bring AI to its solutions ahead of its arch-rival so it could close that gap. SK Hynix likely has similar ambitions. It barely made it into the top five last year, accounting for five percent of the global image sensor market."
A few places stuck out that were mentioned by Coretech on compatibilities with Core Optics when the merger was announced 3/14, LIDAR, military.
**On-sensor AI technology** is an exciting advancement that merges the capabilities of artificial intelligence (AI) with image sensors. Let's delve into the details:
1. **What Is On-Sensor AI Technology?** - **Definition**: On-sensor AI technology involves embedding an **image sensor directly onto an AI chip**. Unlike traditional sensors that relay image information to the CPU for computation and inference, on-sensor AI technology processes data **directly at the sensor level**. - **Purpose**: By integrating AI processing directly into the sensor, this technology enables real-time analysis and decision-making, making it ideal for applications where speed and efficiency matter.
2. **Why Is It Significant?** - **Efficiency**: Traditional image sensors capture raw data, which then undergoes separate AI processing. On-sensor AI technology streamlines this process, reducing latency and enhancing efficiency. - **Real-Time Insights**: With AI algorithms running directly on the sensor, systems can extract insights in near real time. This capability is crucial for applications like autonomous vehicles, surveillance, and smart devices. - **Autonomous Systems**: By building intelligence around each sensor, we pave the way for intelligent and eventually autonomous systems. These systems can continuously learn and adapt based on sensor data.
3. **Use Cases**: - **Smart Cameras**: On-sensor AI enhances image recognition, object tracking, and scene analysis in cameras. - **Automotive**: In self-driving cars, on-sensor AI processes data from LiDAR, radar, and cameras to make split-second decisions. - **Healthcare**: Medical imaging sensors can benefit from on-sensor AI for faster diagnosis and treatment planning. - **Industrial Automation**: Sensors embedded with AI can monitor machinery health, predict failures, and optimize production processes.
4. **Challenges**: - **Data Volume**: High-resolution data (sometimes down to nanosecond precision) is essential for real-time analytics. Balancing data volume with processing power is critical. - **Integration**: Ensuring seamless integration of AI processing within the sensor design is a complex engineering task.
In summary, on-sensor AI technology represents a leap forward in making our world smarter, more connected, and responsive through the fusion of AI and sensor data¹².
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