New AI Chip Developed Will Empower Edge Devices.
New AI Chip Developed – Researchers at the University of Southern California has developed a new chip that addresses AI’s hardware bottleneck and could give edge device server-like AI processing capability without the need for data to transit a network.
Hardware has become the bottleneck of AI, according to USC Professor of Electrical and Computer Engineering, Joshua Yang, whose research with collaborators might change that.
The research team believe that they have developed a new type of chip with the best memory of any chip for edge AI. Their new paper in Nature focuses on the understanding of fundamental physics that leads to a drastic increase in memory capacity needed for AI hardware.
The team led by Yang, which includes researchers from USC (including Han Wang’s group), MIT, and the University of Massachusetts, developed a protocol for devices to reduce processing noise and demonstrated the practicality of using this protocol in integrated chips. This demonstration was made at TetraMem, a startup company co-founded by Yang and his co-authors (Miao Hu, Qiangfei Xia, and Glenn Ge), to commercialize AI acceleration technology.
According to Yang, this new memory chip has the highest information density per device (11 bits) among all types of known memory technologies. Such small but powerful devices could play a critical role in bringing incredible power to the many devices. The chips are not just for memory but also for the processor. And millions of them in a small chip, working in parallel to rapidly run AI tasks, will only require a small battery for power.
The chips that Yang and his colleagues have developed combine silicon with metal oxide memristors to build powerful but low-energy intensive chips. The technique focuses on using the positions of atoms to represent information rather than the number of electrons, which is the current technique involved in computations on chips.
New AI Chip Developed For Edge Devices
The positions of the atoms offer a compact and stable way to store more information in an analogue, instead of digital fashion. Moreover, the information can be processed where it is stored, instead of being sent to one of the few dedicated processors, eliminating the ‘von Neumann bottleneck’ existing in current computing systems and making devices more efficient.
Yang explains that electrons which are manipulated in traditional chips, are ‘light’ and this lightness, makes them prone to moving around and being more volatile. Instead of storing memory through electrons, Yang and collaborators are storing memory in full atoms.
Here is why this memory matters. Normally, says Yang, when one turns off a computer, the information memory is gone – but if you need that memory to run a new computation and your computer needs the information all over again, you have lost both time and energy. This new method, focusing on activating atoms, rather than electrons, does not require battery power to maintain stored information.
Similar scenarios happen in AI computations, where a stable memory capable of high information density is crucial. Yang imagines this new tech that may enable powerful AI capability in edge devices, such as Google Glasses, which he says previously suffered from a frequent recharging issue.
Further, by converting chips to rely on atoms as opposed to electrons, chips become smaller. Yang adds that with this new method, there is more computing capacity at a smaller scale. And this method, he says, could offer “many more levels of memory to help increase information density”.
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“New AI Chip Developed Will Empower Edge Devices.”
