Implementing algorithms on a fault-tolerant quantum computer will require fast decoding
throughput and latency times to prevent an exponential increase in buffer times between the …

Binarized Neural Networks (BNN), which significantly reduce computational complexity and
memory demand, have shown potential in cost-and power-restricted domains, such as IoT …

Despite foreseeing tremendous speedups over conventional deep neural networks, the
performance advantage of binarized neural networks (BNNs) has merely been showcased …

Deep convolutional Neural Networks (CNNs) have revolutionized numerous applications,
but the demand for ever more performance remains unabated. Scaling CNN computations to …

Binarized neural networks (or BNNs) promise tremendous performance improvement over
traditional DNNs through simplified bit-level computation and significantly reduced memory …

The automatic facial expression tracking method has become an emergent topic during the
last few decades. It is a challenging problem that impacts many fields such as virtual reality …

Convolutional Neural Networks (CNNs) are the state-of-the-art Artificial Intelligence
frameworks that are used for a variety of applications including autonomous driving …

Binary neural networks (BNNs) show promising utilization in cost and power-restricted
domains such as edge devices and mobile systems. This is due to its significantly less …

In the last decade, Artificial Intelligence (AI) through Deep Neural Networks (DNNs) has
penetrated virtually every aspect of science, technology, and business. Many types of DNNs …

Recently, the recurrent neural network, or its most popular type—the Long Short Term
Memory (LSTM) network—has achieved great success in a broad spectrum of real-world …