Over the past decade, a massive proliferation of machine learning algorithms has emerged,
from applications for surveillance to self-driving cars. The turning point occurred with the …

Convolutional Neural Networks (CNN) are widely adopted for Machine Learning (ML) tasks,
such as classification and computer vision. GPUs became the reference platforms for both …

GPUs became the reference platform for both training and inference phases of
Convolutional Neural Networks (CNN) due to their tailored architecture to the CNN …

Applications such as autonomous driving or assistive robotics heavily rely on the usage of
Deep Neural Networks. In particular, Convolutional Neural Networks (CNNs) provide …

The optimization for hardware processor and system for performing deep learning
operations such as Convolutional Neural Networks (CNN) in resource limited embedded …

The hardware accelerator controlled by direct memory access (DMA) is greatly influenced by
the communication bandwidth from/to DRAM through on-chip buses. This paper proposes a …

The performance of a hardware accelerator is often limited by the communication bandwidth
between local on-chip memories and DRAM across on-chip bus. In this paper, a system …

In this paper, a new pre-RTL simulator is proposed to predict the power, performance, and
area of convolutional neural network (CNN) dataflows prior to register-transfer-level (RTL) …

Multicore accelerators have emerged to efficiently execute recent applications with complex
computational dimensions. Compared to a single-core accelerator, a multicore accelerator …

Machine Learning (ML) is a sub-area of artificial intelligence comprehending algorithms to
solve classification and pattern recognition problems. One of the most common ways to …