The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

Transformer models have become a dominant architecture in the world of machine learning.
From natural language processing to more recent computer vision applications …

This paper presents a spiking neural network (SNN) accelerator made using fully open-
source EDA tools, process design kit (PDK), and memory macros synthesized using Open …

Tensor processing units (TPUs) are one of the most well-known machine learning (ML)
accelerators utilized at large scale in data centers as well as in tiny ML applications. TPUs …

Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms
of energy consumption and latency when performing inference with deep learning …

Tensor processing units (TPUs), specialized hardware accelerators for machine learning
tasks, have shown significant performance improvements when executing convolutional …

Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms
of energy consumption and latency when performing inference with deep learning …

Acceleration techniques play a crucial role in enhancing the performance of modern high-
speed computations, especially in Deep Learning (DL) applications where the speed is of …

As the technology industry is moving towards implementing tasks such as natural language
processing, path planning, image classification, and more on smaller edge computing …

Rapid growth in Deep Neural Network (DNN) workloads has increased the energy footprint
of the Artificial Intelligence (AI) computing realm. For optimum energy efficiency, we propose …