Priyanka Raina and H.S. Philip Wong’s NeuRRAM chip
Their chip is twice as energy efficient, versatile, and delivers results that are as accurate as conventional digital chips.
AI-powered edge computing is already pervasive in our lives. Devices like drones, smart wearables, and industrial IoT sensors are equipped with AI-enabled chips so that computing can occur at the “edge” of the internet, where the data originates. This allows real-time processing and guarantees data privacy.
However, AI functionalities on these tiny edge devices are limited by the energy provided by a battery. Therefore, improving energy efficiency is crucial. In today’s AI chips, data processing and data storage happen at separate places – a compute unit and a memory unit. The frequent data movement between these units consumes most of the energy during AI processing, so reducing the data movement is the key to addressing the energy issue.
Stanford University engineers have come up with a potential solution: a novel resistive random-access memory (RRAM) chip that does the AI processing within the memory itself, thereby eliminating the separation between the compute and memory units. Their “compute-in-memory” (CIM) chip, called NeuRRAM, is about the size of a fingertip and does more work with limited battery power than what current chips can do.
“Having those calculations done on the chip instead of sending information to and from the cloud could enable faster, more secure, cheaper, and more scalable AI going into the future, and give more people access to AI power,” states Philip, the Willard R. and Inez Kerr Bell Professor in the School of Engineering.
“The data movement issue is similar to spending eight hours in commute for a two-hour workday,” added Weier Wan, a recent graduate at Stanford leading this project. “With our chip, we are showing a technology to tackle this challenge.”
They presented NeuRRAM in a recent article in the journal Nature. While compute-in-memory has been around for decades, this chip is the first to actually demonstrate a broad range of AI applications on hardware, rather than through simulation alone.
Excerpted from, “Stanford engineers present new chip that ramps up AI computing efficiency” Stanford News.