Spiking neural networks (SNNs) often are touted as a way to get close to the power efficiency of the brain, but there is widespread confusion about what exactly that means. In fact, there is disagreement about how the brain actually works. Some SNN implementations are less brain-like than others. Depending on whom you talk to, SNNs are either a long way away or close to commercialization. The varying definitions of SNNs leads to differences in how the industry is seen. "A few startups are doing their own SNNs," said Ron Lowman, strategic marketing manager of IP at Synopsys. "It's being driven by guys that have expertise in how to train, optimize, and write software for them." On the other hand, Flex Logix Inference Technical Marketing Manager Vinay Mehta said that, "SNNs are out further than reinforcement learning," referring to a machine-learning concept that's still largely in the research phase. The entire notion of a "neural network" is motivated by attempts to model how the brain works.

At the IEEE International Electron Devices Meeting (IEDM) in San Francisco this week, France-based research institute CEA-Leti presented papers highlighting its achievements in bio-inspired neural networks, a readout technique for high-fidelity measurements in large quantum dot arrays and inorganic thin film batteries with optimum energy and power density performance for medical and implantable devices. This article presents highlights of each of these three papers. Bio-inspired neural networks have been in development for a while, and at IEDM, Leti announced it had fabricated a fully integrated bio-inspired neural network, combining resistive-RAM-based synapses and analog spiking neurons. The functionality of this proof-of-concept circuit was demonstrated thanks to handwritten digits classification. "The entire network is integrated on-chip," said Alexandre Valentian, lead author of the paper, Fully Integrated Spiking Neural Network with Analog Neurons and RRAM Synapses.

In the world of AI research, Europe has drawn a line in the sand, declaring that R&D must focus squarely on "Edge AI." This proclamation draws a stark contrast to "Cloud-based AI," the model aggressively pursued by China and the United States. During "Innovation Days" hosted here by French research institute CEA-Leti this past week, Emmanuel Sabonnadiere, CEA-Leti's CEO, discussed the "two schools of AI research" that have split the world in two. Both the U.S. and China have been collecting massive amounts of data which they use for training AIs, the basis for their claims they lead the world AI race. Strict data privacy regulations in Europe might be seen as impeding European companies' progress in AI, but that's not necessarily the case.