Abstract Design of hardware accelerators for neural network (NN) applications involves
walking a tight rope amidst the constraints of low-power, high accuracy and throughput …

Abstract Machine learning (ML) methods has recently contributed very well in the
advancement of the prediction models used for energy consumption. Such models highly …

Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of
application domains such as scientific computing and graph analytics. Due to its intrinsic …

Disclosed embodiments relate to an accelerator for sparse dense matrix instructions. In one
example, a processor to execute a sparse-dense matrix multiplication instruction, includes …

Sparse matrix operations are critical kernels in multiple application domains such as High
Performance Computing, artificial intelligence and big data. Vector processing is widely …

Many accidents and serious problems occur on the road due to the rapid increase in traffic
congestion in all sections of the country. Autonomous vehicles provide a solution to …

Deep convolutional neural networks (DNNs) have been widely used in many applications,
particularly in machine vision. It is challenging to accelerate DNNs on embedded systems …

Deep neural network compression is important and increasingly developed especially in
resource-constrained environments, such as autonomous drones and wearable devices …

Abstract Convolutional Neural Networks (CNNs) currently dominate the fields of artificial
intelligence and machine learning due to their high accuracy. However, their computational …

Deep learning is nowadays one of the most popular research topics in computer science. In
recent years, the extensive application of convolutional neural network has made it become …