The rapid development of deep learning algorithms, as well as innovative hardware
advancements, encourages multi-DNN workloads such as augmented reality applications …

As machine learning and AI continue to rapidly develop, and with the ever-closer end of
Moore's law, new avenues and novel ideas in architecture design are being created and …

In recent years, many accelerators have been proposed to efficiently process sparse tensor
algebra applications (eg, sparse neural networks). However, these proposals are single …

Attention-based neural networks have become pervasive in many AI tasks. Despite their
excellent algorithmic performance, the use of the attention mechanism and feedforward …

Due to complex interactions among various deep neural network (DNN) optimization
techniques, modern DNNs can have weights and activations that are dense or sparse with …

Nowadays, artificial intelligence (AI) technologies, especially deep neural networks (DNNs),
play an vital role in solving many problems in both academia and industry. In order to …

This article presents an 8k-multiply-accumulate (MAC) neural processing unit (NPU) in 4-nm
mobile system-on-chip (SoC). The unified multi-precision MACs support from integer (INT) …

ACM: Digital Library: Communications of the ACM ACM Digital Library Communications of the
ACM Volume 67, Number 2 (2024), Pages 86-97 Energy and Emissions of Machine Learning on …

Non-linear operations such as GELU, Layer normalization, and Soft-max are essential yet
costly building blocks of Transformer models. Several prior works simplified these …

Deep Learning (DL) acceleration support in CPUs has recently gained a lot of traction, with
several companies (Arm, Intel, IBM) announcing products with specialized matrix engines …