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, ...

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

Scientists in the US have created a tiny silicon chip that can perform mathematical ...

Engineers at MIT have turned one of computing’s biggest headaches, waste heat, into the main act. By sculpting “dust-sized” silicon structures that steer heat as precisely as electrical current, they ...

Abstract: Matrix-vector multiplication is practically used in all Digital Signal Processing (DSP) algorithms. Particularly, in the channel emulation field, it is required to perform this algorithm in ...

Abstract: On multicore architectures, the ratio of peak memory bandwidth to peak floating-point performance (byte:flop ratio) is decreasing as core counts increase, further limiting the performance of ...

Researchers at Massachusetts Institute of Technology have demonstrated a surprising new way to compute—by using heat instead ...

A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...

A research team has successfully implemented a programmable spinor lattice on a photonic integrated circuit (PIC). This platform enables the realization of non-Abelian physics, in which the outcome of ...

Weijia Shang received BS degree in computer engineering from Changsha Institute of Technology, China, and Master and Ph.D. degrees in computer engineering from Purdue University, West Lafayette, ...