Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …

Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …

Text-to-image generation has traditionally focused on finding better modeling assumptions
for training on a fixed dataset. These assumptions might involve complex architectures …

New developments in hardware-based 'accelerators' range from electronic tensor cores and
memristor-based arrays to photonic implementations. The goal of these approaches is to …

Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …

Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural
network training. However, existing 4-bit training methods require custom numerical formats …

Convolutional neural networks (CNNs) are used in our daily life, including self-driving cars,
virtual assistants, social network services, healthcare services, and face recognition, among …

When quantizing neural networks for efficient inference, low-bit integers are the go-to format
for efficiency. However, low-bit floating point numbers have an extra degree of freedom …

Improving the deployment efficiency of transformer-based language models has been
challenging given their high computation and memory cost. While INT8 quantization has …