The move to RISC-V for running neural networks for edge AI applications is accelerated by the proposed takeover of ARM by Nvidia, says Yann LeCun, chief AI scientist at Facebook speaking at the Innovation Day of French research lab CEA-Leti.
“There is a change in the industry and ARM with Nvidia makes people uneasy but the emergence of RISC-V sees chips with a RISC-V core and an NPU (neural processing unit),” he said. “These are incredibly cheap, less than $10, with many out of China, and these will become ubiquitous,” he said. “I’m wondering if RISC-V will take over the world there.”
He is dismissive of a major programme at Leti working on spiking neural networks and analogue approaches such as resistive RAM (RRAM), but this might be expected from the inventor of the Convolutional Neural Network (CNN) and winner of the Turing Award for AI in 2018.
“The main problems that analogue implementations face is its very difficult to use hardware multiplexing with analogue neural nets,” he said. “When you do a convolution and reuse the hardware, you have to do hardware multiplexing and so you have to have a way to store the results and then you need analogue memory or ADC and DAC converters and that kills the entire idea. So unless we have cheap low power analogue memory that’s not going to work,” he said. “I’m doubtful, perhaps that will be memristor arrays or spintronics, but I’m somewhat sceptical.”
“Certainly edge AI is a super important topic,” he said. “In the next two to three years, it’s not going to be exotic technologies, it’s about reducing the power consumption as much as possible, pruning the neural net, optimising the weights, shutting down parts of the system that aren’t used," said LeCun. "The target is AR devices with chips in the next two to three years with devices in the five years, and that’s coming,” he said.