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SiOx ReRAMs Promise to Accelerate AI Self-Learning
eetasia.com, Sept. 03, 2020 –
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.
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