Distributed computing has markedly advanced the efficiency and reliability of complex numerical tasks, particularly matrix multiplication, which is central to numerous computational applications from ...

ATLANTA--(BUSINESS WIRE)--d-Matrix today officially launched Corsair™, an entirely new computing paradigm designed from the ground-up for the next era of AI inference in modern datacenters. Corsair ...

Startup launches “Corsair” AI platform with Digital In-Memory Computing, using on-chip SRAM memory that can produce 30,000 tokens/second at 2 ms/token latency for Llama3 70B in a single rack. Using ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Chinese researchers have made a significant breakthrough in the field of computing by developing a high-precision scalable analog matrix computing chip. This new analog chip is touted to be 1,000 ...

The initiative expands access to neuromorphic computing, a system that uses chips that mimic the human brain to save energy.

Photonics is promising to handle extensive vector multiplications in AI applications. Scientists in China have promoted a programmable and reconfigurable photonic linear vector machine named SUANPAN, ...