Fullmoonfever
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Little blog post from a self described tech head when she was at the recent summits.
Had to post as I liked that we were running NVISO at the summit and the pic showing fps with Akida H/W
We have seen the face and face feature detection at a number of booths. Here is a photo taken at the BrainChip booth.
The neuromorphic processor IP, Akida™, mimics the human brain to analyze essential sensor inputs at the acquisition point. It is real-time inference and learning at the edge by Akida’s fully customizable event-based AI neural processor. By inferencing, the chip concluded that the face above is in the “Happiness” mode. The other available modes are “Neutral” and “Sadness.”
Object detection is important in edge AI. Here are two aspects of its current status:
Had to post as I liked that we were running NVISO at the summit and the pic showing fps with Akida H/W
AI Frontiers in 2022
Takeaways from AI Hardware Summit and Edge AI Summit 2022
betterprogramming.pub
Takeaways from AI Hardware Summit and Edge AI Summit 2022
AI Chips Can Detect Human Emotions
Edge computing can happen on IoT devices. There are traditional AI models, such as Regression Analysis, Logistic Regression, Neural Networks, Support Vector Machines, Multiclass Classification, and K-Means Clustering. Edge AI models are more task-specific, such as general detectors, high-speed detectors, classifiers, densities, re-identifications, personal protective equipment (PPE), thermal detectors, face detection, face identification, face feature detection, scene segmentation, and skeleton detectors.We have seen the face and face feature detection at a number of booths. Here is a photo taken at the BrainChip booth.
The neuromorphic processor IP, Akida™, mimics the human brain to analyze essential sensor inputs at the acquisition point. It is real-time inference and learning at the edge by Akida’s fully customizable event-based AI neural processor. By inferencing, the chip concluded that the face above is in the “Happiness” mode. The other available modes are “Neutral” and “Sadness.”
Object detection is important in edge AI. Here are two aspects of its current status:
- Accuracy: The original images obtained from IoT devices may be distorted by reflection, blur, soiling, snow, rain, fog, etc. It requires calibration for object recognition and classification. Model accuracy continuously improves.
- Efficiency: The image analytics need to be real time and likely in a high frame rate. It includes geographic information system (GIS) calibration and object tracking. Edge computing reduces server latency and bandwidth.