PINNs, like other Deep Neural Network (DNN) based models, are typically quantized to reduce its memory footprint and allow models to run on resource (compute, memory, bandwidth, power, etc.) ...

Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets ...

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...

Quantization is a compression technique used in image processing that reduces a range of values to a single quantum value. It is the process of converting a sampled image with real values to one with ...

We study the perceptual problem related to image quantization from an optimization point of view, using different metrics on the color space. A consequence of the results presented is that ...

School of Electrical and Computer Engineering, Cornell Tech, New York, NY, United States Spiking neural networks (SNNs) have received increasing attention due to their high biological plausibility and ...