Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...

Combinatorial optimization (CO) underpins critical applications in science and engineering, ranging from logistics to electronic design automation. A classic example of CO is the NP-complete Traveling ...

In today's volatile business landscape, relying on sporadic flashes of inspiration can be a fragile strategy for maintaining a competitive edge. The truth is, creativity is not a mystical gift ...

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a widely used algorithm in Machine Learning, particularly in the increasingly popular Graph Neural Networks (GNNs). SpMM is an essential ...

Fractions are a foundational piece for tackling mathematics at all levels of schooling. Students need to understand how two numbers interact with each other as numerators and denominators, as ratios, ...

The irregular distribution of non-zero elements of large-scale sparse matrix leads to low data access efficiency caused by the unique architecture of the Sunway many-core processor, which brings great ...

Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.

In constructing the Support Vector Regression (SVR) model, the Radial Basis Function (RBF) kernel was selected. Due to the significant influence of the penalty factor C and the RBF kernel function ...

Computer scientists are a demanding bunch. For them, it’s not enough to get the right answer to a problem — the goal, almost always, is to get the answer as efficiently as possible. Take the act of ...

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of ...