SK hynix, TetraMem, and the University of Southern California built a memristor-based in-memory computing system-on-chip for ...

Abstract: Graph convolutional networks (GCNs) are emerging neural network models designed to process graph-structured data. Due to massively parallel computations using irregular data structures by ...

The CEO of Digitas on why overpromising with AI is a dangerous path for marketers.

Dear Carolyn: We have three grandsons around the same age (one by each of our three children). We love them all dearly. Two of our kids are pretty unashamed about asking us for regular babysitting and ...

Tensordyne says logarithmic computing could reduce AI inference costs and power demands, offering an alternative to conventional chip designs.

Abstract: Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n 3) for matrix ...