2024 BrainChip Discussion, page-6294

Thanks everyone who got involved with the post above. I am exploring alternatives to ChatGPT now.

Back to the BRN newsletter, I felt it would have helped if it discussed an example of a device in more detail so I asked GPT the following.

QUESTION to ChatGPT:
from the following in a BRN newsletter, "ENNs addresses the significant energy and computational demands of current AI systems. This technology excels in handling sequential and continuous data streams for edge and event-based processing, making it perfect for smart home, automotive, healthcare, and industrial applications.TENNs integrates state space models developed in parallel and generalizes convolution kernels to handle various transformer tasks, such as processing language, time-series, and spatiotemporal data. This broadens its applicability while significantly reducing energy consumption compared to traditional models.",
please give a for-instance application starting with 'suppose you want to make a device to detect skin cancer'

ChatGPT

Suppose you want to make a device to detect skin cancer. This device needs to process continuous data streams, such as real-time images of skin lesions, and make accurate predictions based on this data while minimizing energy consumption.

To achieve this, you could utilize ENNs (Edge Neural Networks) technology. ENNs are designed to handle the significant energy and computational demands typical of current AI systems, making them ideal for healthcare applications where efficient and reliable data processing is crucial. By leveraging ENNs, your skin cancer detection device could process the continuous stream of images from a camera, analyzing each frame for signs of cancerous changes in the skin.

The device would benefit from the event-based processing capabilities of ENNs, which means it can efficiently handle the real-time data without needing to send all the information to a central server, thus reducing latency and improving responsiveness. This edge processing capability is crucial in healthcare applications where timely decisions can significantly impact patient outcomes.

Furthermore, by integrating TENNs (Transformer-Enhanced Neural Networks), your device can employ state space models that have been developed in parallel, enhancing its ability to process complex data like skin lesion images. TENNs generalize convolution kernels, which are typically used in image processing, to handle various transformer tasks. This means the device can not only process the visual data efficiently but also adapt to different types of data inputs, such as patient history or other time-series health data.

By using TENNs, your skin cancer detection device would significantly reduce energy consumption compared to traditional models, making it more suitable for portable or wearable applications. This efficiency ensures the device can operate for extended periods without frequent recharging, enhancing its usability and convenience for continuous monitoring.

In summary, by employing ENNs and TENNs, your skin cancer detection device can efficiently process continuous image data in real-time, reduce energy consumption, and maintain high accuracy, making it a powerful tool in the early detection and monitoring of skin cancer.