Abstract: In modern machine learning models like Transformers, matrix multiplication dominates most computation. Specific hardware often uses large-scale PE arrays, such as systolic arrays, to ...

Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...

The prediction that transistor counts on microchips would keep doubling every two years gave the tech industry its growth ...

Imagine a problem so fiendishly hard that one of the greatest mathematicians of the 20th century publicly mused that even alien civilisations ...

Today, on International Women's Day and at Embedded World, Ambient Scientific has announced its partnership with Dimension ...

Collaboration targets real-time on-device generative AI rendering for mobile devices and creative tools, optimized for performance and efficiency across device tiers. BARCELONA, Spain, /PRNewswire/ -- ...

Those that solve artificially simplified problems where quantum advantage is meaningless. Those that provide no genuine quantum advantage when all costs are properly accounted for. This critique is ...

Oracle-based quantum algorithms cannot use deep loops because quantum states exist only as mathematical amplitudes in Hilbert space with no physical substrate. Criticall ...

AI is beginning to make inroads into designing and managing programmable logic, where it can be used to simplify and speed up portions of the design process. FPGAs and DSPs are st ...