New research reveals why even state-of-the-art large language models stumble on seemingly easy tasks—and what it takes to fix ...

Deploying large language models (LLMs) on resource-constrained devices presents significant challenges due to their extensive parameters and reliance on dense multiplication operations. This results ...

AI training time is at a point in an exponential where more throughput isn't going to advance functionality much at all. The underlying problem, problem solving by training, is computationally ...

MathFormer is an innovative deep learning project that demonstrates how transformer-based neural networks can learn to perform fundamental arithmetic operations. The implementation features ...

Most neural network topologies heavily rely on matrix multiplication (MatMul), primarily because it is essential to many basic processes. Vector-matrix multiplication (VMM) is commonly used by dense ...