Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Convolution neural networks (CNNs) are the heart of deep learning applications. Recent
works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access …

Autonomous driving systems have attracted a significant amount of interest recently, and
many industry leaders, such as Google, Uber, Tesla, and Mobileye, have invested a large …

Deep Neural Networks (DNNs) are compute-intensive learning models with growing
applicability in a wide range of domains. FPGAs are an attractive choice for DNNs since they …

Disaggregated memory can address resource provisioning inefficiencies in current
datacenters. Multiple software runtimes for disaggregated memory have been proposed in …

DNNs (Deep Neural Networks) have demonstrated great success in numerous applications
such as image classification, speech recognition, video analysis, etc. However, DNNs are …

Graphs are one of the key data structures for many real-world computing applications and
the importance of graph analytics is ever-growing. While existing software graph processing …

While systolic array architectures have the potential to deliver tremendous performance, it is
notoriously challenging to customize an efficient systolic array processor for a target …