Over the past decade, the dominance of deep learning has prevailed across various
domains of artificial intelligence, including natural language processing, computer vision …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …

The energy efficiency of deep neural network (DNN) inference can be improved with custom
accelerators. DNN inference accelerators often employ specialized hardware techniques to …

Transfer learning in natural language processing (NLP) uses increasingly large models that
tackle challenging problems. Consequently, these applications are driving the requirements …

Abstract Straight-through estimator (STE), which enables the gradient flow over the non-
differentiable function via approximation, has been favored in studies related to quantization …

We present unit scaling, a paradigm for designing deep learning models that simplifies the
use of low-precision number formats. Training in FP16 or the recently proposed FP8 formats …

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML)
algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise …

Large speech models are rapidly gaining traction in research community. As a result, model
compression has become an important topic, so that these models can fit in memory and be …

Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer
vision. However, due to their high latency, the deployment of DNNs hinges on the …

The upscaling of Large Language Models (LLMs) has yielded impressive advances in
natural language processing, yet it also poses significant deployment challenges. Weight …