The rapid growth of artificial intelligence and the increasing complexity of neural network models are driving demand for efficient hardware architectures that can address power-constrained and ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

Everything around you is getting smarter. Artificial intelligence is not just a data center application but will be deployed in all kinds of embedded systems that we interact with daily. We expect to ...