Ai has been going on in the background for 3 years and they just won an award for most innovative design, So you cannot ignore that it goes hand in hand with a brand new way of doing things with reram.
Do you say dont worry about it then someone else comes along and develops it.
I mention NASA because they took weebits onto space already to prove it handles radition. I"m thinking down the track for the uses of reram.
Sometimes when you invent something thats game changing you run with the opertunities that are presented, take a big bite and chew like hell. I understand were you are coming from regarding focusing on embedded and descete and were the focus changed but this comes down to the leti selector and the new power saving transistor that opens up a whole new can of operating opportunities for Ai. In the article below it says


CEO Coby Hanoch, as the company has been working closely with Ielmini and his team for more than three years.

Considering when Weebit listed on ASX its been a goal the whole time.

Home » AI » SiOx ReRAMs Promise to Accelerate AI Self-Learning

SiOx ReRAMs Promise to Accelerate AI Self-Learning

Article By : Gary Hilson

• Category : AI

2020-09-03 (0) Comments

Researchers at Politecnico Milan used Weebit ReRAM provide inference hardware with brain-like plasticity...

Recent research using Weebit Nano’s silicon oxide (SiOx) ReRAM technology outlines a brain-inspired artificial intelligence (AI) system which can perform unsupervised learning tasks with high accuracy results.

The work was done by researchers at Politecnico Milan (the Polytechnic University of Milan) and presented in a recent joint paper with the company that details a novel AI self-learning demonstration based on Weebit’s SiOx ReRAM. The memory technology is considered a prime candidate to succeed NAND flash memory because of its potential to be 1,000 times faster while using 1,000 times less energy than NAND, while at the same time lasting 100 times longer. Weebit’s SiOx ReRAM is also appealing because it can leverage existing manufacturing processes.

ReRAM has also been eyed for AI applications by several research organizations. The university developed a hardware design that uses Weebit’s ReRAM to combine the efficiency of convolutional neural networks (CNNs) with the plasticity of brain-inspired spiking neural networks (SNN) to enable the hardware to learn new things without forgetting trained tasks of previously acquired information. In addition, the system adapts its operative frequency for power saving, enabling feasible solutions for lifelong learning in autonomous AI systems.

Weebit’s ReRAM cell consists of two metal layers with a silicon oxide (SiOx) layer between them comprised of materials that can used in existing production lines. (Source: Weebit)

The research by professor Daniele Ielmini and his team looks at the inability of artificial neural networks (ANNs) to acquire new information without forgetting trained tasks, even though they outperform the human ability of object recognition. The team’s SiOx ReRAM-based inference hardware was able to merge the efficiency of convolutional ANNs and the plasticity of spiking networks. In an interview with EE Times, Ielmini said the research demonstrates that the circuit plastically adapts its operative frequency for power saving and enables continual learning of up to 50% non-trained classes. This optimizes the classification and enables the re-training of the filters, thus overcoming the catastrophic forgetting of standard ANNs, he said.

Ielmini said the biggest challenge for AI hardware to date has been limitations on what it can learn. For example, if the hardware is trained to recognize certain digits, it can only recognize the digits it was trained for, but not recognize any additional digits. Similarly, it won’t be able recognize letters on its own because it was exposed to digits. The purpose of the research was to develop a new hardware based on ReRAM that can continually learn, he said, and it showed that their inference could learn 50% more based on what it was already taught. For example, it could train the hardware on 100 figures, and it could recognize an additional 100 figures without being trained. “This is exactly what happens in the brain when we learn something.”

Essentially, when the brain sees something it recognizes, said Ielmini, there’s a neuron representing the target that spikes. Every time that neuron fires, it spends energy. Because the brain wants to be energy efficient, there is internal feedback which reduces the threshold for the neuron to spike, which ultimately allows for lifelong learning, he said, which the researchers we were able to mimic with their hardware using ReRAM, while achieving high energy efficiency within the system. “This is a big limit of AI hardware nowadays.”

For its part, Weebit Nano wasn’t surprised by the results achieved in Milan, said CEO Coby Hanoch, as the company has been working closely with Ielmini and his team for more than three years. “It is important for us to show that our SiOx ReRAM can serve not only as an advanced memory but also enable other advanced applications.” Weebit Nano has always strongly believed that ReRAM has big potential for neuromorphic applications and other advanced applications, he said.

The company engages with many researchers on potential applications for ReRAM. Hanoch said this research with Polimi research adds plasticity to the current AI systems. “The common approach to AI today is based on supervised learning where you have to spend significant efforts to train the system, and once trained it can only perform the task that it was trained for.” The human brain, however, can classify objects without being trained massively, he since it has plasticity and is able to project from only few images.