Brain-inspired chips can slash AI energy use by as much as 100-fold, but the road to mainstream deployment is far from ...

For example, when using the quantum processor to reconstruct lightning data, they found it did a better job at lower altitudes but was generally comparable to the classical neural network.

Aimed at AI/ML applications, Renesas’ RA8P1 MCUs leverage an Arm Ethos-U55 neural processing unit (NPU) delivering 256 GOPS ...

The report, entitled “BrainChip Akida is a Fast Learner – Spiking-Neural-Network Processor Identifies Patterns in Unlabeled Data,” is available for free download from the BrainChip web site.

Intel is not alone in its quest to speed up neural networks. Google has developed cloud-based silicon called Tensor Processing Units, and Nvidia is pushing its GPUs as a solution to neural network ...

In 2017, MIT researchers succeeded in creating a chip that can run neural network calculations, but there were some notable limitations. Most of the heavy lifting in machine learning comes in the ...

On the hardware front, Baidu is collaborating with Intel on the research and development of Nervana Neural Network Processor for Training (NNP-T), a hardware accelerator optimized for deep learning.

The network achieved more than 96% accuracy in training, which is close to traditional computing. When running machine learning workloads, the photonic system achieved 92% accuracy.