- BrainChip (BRN) launches the second generation of its Akida neuromorphic artificial intelligence platform
- The system now includes temporal event-based neural nets that enable it to learn from raw sensor data
- It also includes Vision Transformer acceleration that works on computer vision tasks, such as image classification, object detection, and semantic segmentation
- BRN says the second-generation tech adds capabilities “critically needed” in industrial, automotive, digital health, smart home and smart city applications
- Shares in BrainChip are up 19.12 per cent and trading at 61 cents at 3:14 pm AEDT
BrainChip’s (BRN) share price soared today after the company launched the second generation of its Akida neuromorphic artificial intelligence platform.
The system now includes temporal event-based neural nets that the company said “supercharge” the processing of raw time-continuous streaming data.
This includes video analytics, target tracking, audio classification, analysis of health monitoring data — such as heart rate and respiratory rate for vital signs prediction and time series analytics used in forecasting — and predictive production line maintenance.
The second-generation tech also includes Vision Transformer acceleration, which BrainChip said worked on various computer vision tasks, such as image classification, object detection, and semantic segmentation.
BrainChip CEO Sean Hehir said the company had used customer feedback in developing the new generation of Akida.
“Our customers wanted us to enable expanded predictive intelligence, target tracking, object detection, scene segmentation, and advanced vision capabilities,” Mr Hehir said.
“By inferring and learning from raw sensor data, we take a substantial step toward a cloudless Edge AI experience.”
The platform is used for intelligent edge devices for the Artificial Intelligence of Things
(AIoT) solutions and services market that BrainChip forecasts to be worth US$1 trillion by 2030.
BRN shares were up 19.12 per cent and trading at 61 cents at 3:14 pm AEDT.